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Gravitational Dynamic Radius Nearest Neighbor Trained by Fuzzy Enhanced Hiking Optimization Algorithm for Imbalanced Data Classification 认领 引用
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作者 Zahra Beheshti 《Journal of Bionic Engineering》 SCIE EI CSCD 2026年第3期1826-1848,共23页
Imbalanced data classification is one of the most critical challenges in machine learning.Standard classifiers do not provide adequate accuracy for detecting minority samples because these classifiers are biased towar... Imbalanced data classification is one of the most critical challenges in machine learning.Standard classifiers do not provide adequate accuracy for detecting minority samples because these classifiers are biased toward the majority samples.To overcome this drawback,many methods have been proposed.One prominent method is the Gravitational Fixed Radius Nearest Neighbor(GFRNN)algorithm,which applies Newton’s law of universal gravitation to determine the class of a test sample based on two parameters:mass and radius.Although GFRNN shows good performance on some imbalanced datasets,it faces several fundamental problems,including ignoring the data distribution and the improper calculation of radius and mass.In this study,a Gravitational Dynamic Radius Nearest Neighbor trained by a Fuzzy Enhanced Hiking Optimization Algorithm(FEHOA-GDRNN)is proposed to improve GFRNN performance.In FEHOA-GDRNN,Enhanced Hiking Optimization Algorithm(EHOA)applies a new spider web search to find better solution based on a Mamdani Fuzzy Inference System(FIS).FEHOA-GDRNN is evaluated on 40 imbalanced datasets and its results are compared with GDRNN trained by Fuzzy HOA(FHOA-GDRNN),GFRNN trained by HOA(HOA-GFRNN),GFRNN and its various versions(IGFRNN,I-GFRNN,and EGDRNN),Cost-Sensitive Support Vector Machine with an RBF kernel(CS-SVM-RBF),Cost-Sensitive Support Vector Machine with a Linear kernel(CS-SVM-Linear),Cost-Sensitive Naïve Bayes(CS-NB),Binary Decision tree(BDT),Random Forest(RF),Gaussian-Probabilistic Neural Network(GaussianPNN)and Skew-Probabilistic Neural Network(SkewPNN).Moreover,the results of the proposed classifier are compared with those of several Fuzzy K-Nearest Neighbor(FKNN).The results demonstrate that FEHOA-GDRNN outperforms other methods in key metrics,including Average Accuracy(AAcc)and Geometric Mean(GM). 展开更多
关键词 Imbalanced data Classification Gravitational Fixed Radius Nearest Neighbor(GFRNN) Mamdani Fuzzy Inference System Hiking Optimization Algorithm(HOA)
Machine Learning Stroke Prediction in Smart Healthcare:Integrating Fuzzy K-Nearest Neighbor and Artificial Neural Networks with Feature Selection Techniques 认领 引用
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作者 Abdul Ahad Ira Puspitasari +4 位作者 Jiangbin Zheng Shamsher Ullah Farhan Ullah Sheikh Tahir Bakhsh Ivan Miguel Pires 《Computers, Materials & Continua》 SCIE EI 2025年第3期5115-5134,共20页
This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and... This research explores the use of Fuzzy K-Nearest Neighbor(F-KNN)and Artificial Neural Networks(ANN)for predicting heart stroke incidents,focusing on the impact of feature selection methods,specifically Chi-Square and Best First Search(BFS).The study demonstrates that BFS significantly enhances the performance of both classifiers.With BFS preprocessing,the ANN model achieved an impressive accuracy of 97.5%,precision and recall of 97.5%,and an Receiver Operating Characteristics(ROC)area of 97.9%,outperforming the Chi-Square-based ANN,which recorded an accuracy of 91.4%.Similarly,the F-KNN model with BFS achieved an accuracy of 96.3%,precision and recall of 96.3%,and a Receiver Operating Characteristics(ROC)area of 96.2%,surpassing the performance of the Chi-Square F-KNN model,which showed an accuracy of 95%.These results highlight that BFS improves the ability to select the most relevant features,contributing to more reliable and accurate stroke predictions.The findings underscore the importance of using advanced feature selection methods like BFS to enhance the performance of machine learning models in healthcare applications,leading to better stroke risk management and improved patient outcomes. 展开更多
关键词 Fuzzy K-nearest neighbor artificial neural network accuracy precision recall F-measure Chi-Square best search first heart stroke
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Hybrid adaptive machine learning approach for detection and mitigation of GNSS spoofing through enhanced osprey optimization algorithm 认领 引用
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作者 KOTI Sushmitha SANDHYA Rachamalla 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第3期1059-1080,共22页
Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,veloci... Global Navigation Satellite Systems(GNSSs)are the specific term utilized with satellite constellation to acquire regional or global services.GNSS sensors use pseudo-distance measurement to estimate the position,velocity,and time(PVT).Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations.In addition,misleading signals are intentionally used to generate timing and position,and GNSS signal spoofing provides a constant risk to consumers.In past works,the implementation of the Global Positioning System(GPS)in autonomous vehicle navigation might be endangered by spoofing.To mitigate these issues,this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks.The developed model is processed with three phases:data collection,feature extraction,and detection.Initially,the required data is taken from the standard resource.Then,the data is given to the feature extraction phase.The features of the data are retrieved using the principal component analysis(PCA)and t-distributed stochastic neighbor embedding(t-SNE)model.The features obtained from the collected data are transferred to the detection phase.In the final phase,the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP).Enhanced osprey optimization algorithm(EOOA)is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models. 展开更多
关键词 Global Navigation Satellite System(GNSS) detection and mitigation of GNSS t-distributed stochastic neighbor embedding(t-SNE) enhanced osprey optimization algorithm principal component analysis(PCA) hybridized adaptive Bayesian learning and multi-layer perceptron(HABMLP)
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Real-Time Spreading Thickness Monitoring of High-core Rockfill Dam Based on K-nearest Neighbor Algorithm 认领 引用 被引量：4
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作者 Denghua Zhong Rongxiang Du +2 位作者 Bo Cui Binping Wu Tao Guan 《Transactions of Tianjin University》 EI CAS 2018年第3期282-289,共8页
During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and... During the storehouse surface rolling construction of a core rockfilldam, the spreading thickness of dam face is an important factor that affects the construction quality of the dam storehouse' rolling surface and the overallquality of the entire dam. Currently, the method used to monitor and controlspreading thickness during the dam construction process is artificialsampling check after spreading, which makes it difficult to monitor the entire dam storehouse surface. In this paper, we present an in-depth study based on real-time monitoring and controltheory of storehouse surface rolling construction and obtain the rolling compaction thickness by analyzing the construction track of the rolling machine. Comparatively, the traditionalmethod can only analyze the rolling thickness of the dam storehouse surface after it has been compacted and cannot determine the thickness of the dam storehouse surface in realtime. To solve these problems, our system monitors the construction progress of the leveling machine and employs a real-time spreading thickness monitoring modelbased on the K-nearest neighbor algorithm. Taking the LHK core rockfilldam in Southwest China as an example, we performed real-time monitoring for the spreading thickness and conducted real-time interactive queries regarding the spreading thickness. This approach provides a new method for controlling the spreading thickness of the core rockfilldam storehouse surface. 展开更多
关键词 Core rockfill dam Dam storehouse surface construction Spreading thickness K-nearest neighbor algorithm Real-time monitor
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Diagnosis of Disc Space Variation Fault Degree of Transformer Winding Based on K-Nearest Neighbor Algorithm 认领 引用 被引量：1
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作者 Song Wang Fei Xie +3 位作者 Fengye Yang Shengxuan Qiu Chuang Liu Tong Li 《Energy Engineering》 EI 2023年第10期2273-2285,共13页
Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose t... Winding is one of themost important components in power transformers.Ensuring the health state of the winding is of great importance to the stable operation of the power system.To efficiently and accurately diagnose the disc space variation(DSV)fault degree of transformer winding,this paper presents a diagnostic method of winding fault based on the K-Nearest Neighbor(KNN)algorithmand the frequency response analysis(FRA)method.First,a laboratory winding model is used,and DSV faults with four different degrees are achieved by changing disc space of the discs in the winding.Then,a series of FRA tests are conducted to obtain the FRA results and set up the FRA dataset.Second,ten different numerical indices are utilized to obtain features of FRA curves of faulted winding.Third,the 10-fold cross-validation method is employed to determine the optimal k-value of KNN.In addition,to improve the accuracy of the KNN model,a comparative analysis is made between the accuracy of the KNN algorithm and k-value under four distance functions.After getting the most appropriate distance metric and kvalue,the fault classificationmodel based on theKNN and FRA is constructed and it is used to classify the degrees of DSV faults.The identification accuracy rate of the proposed model is up to 98.30%.Finally,the performance of the model is presented by comparing with the support vector machine(SVM),SVM optimized by the particle swarmoptimization(PSO-SVM)method,and randomforest(RF).The results show that the diagnosis accuracy of the proposed model is the highest and the model can be used to accurately diagnose the DSV fault degrees of the winding. 展开更多
关键词 Transformer winding frequency response analysis(FRA)method K-Nearest Neighbor(KNN) disc space variation(DSV)
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GHM-FKNN:a generalized Heronian mean based fuzzy k-nearest neighbor classifier for the stock trend prediction 认领 引用 被引量：1
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作者 吴振峰 WANG Mengmeng +1 位作者 LAN Tian ZHANG Anyuan 《High Technology Letters》 EI CAS 2023年第2期122-129,共8页
Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-n... Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX. 展开更多
关键词 stock trend prediction Heronian mean fuzzy k-nearest neighbor(FKNN)
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Gas-Bearing Reservoir Prediction Using k-nearest neighbor Based on Nonlinear Directional Dimension Reduction 认领 引用
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作者 Song Zhao-Hui Sang Wen-Jing +1 位作者 Yuan San-Yi Wang Shang-Xu 《Applied Geophysics》 SCIE CSCD 2024年第2期221-231,418,共11页
In this study,a k-nearest neighbor(kNN)method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction.The kNN method can select the most relevant training samples to establish... In this study,a k-nearest neighbor(kNN)method based on nonlinear directional dimension reduction is applied to gas-bearing reservoir prediction.The kNN method can select the most relevant training samples to establish a local model according to feature similarities.However,the kNN method cannot extract gas-sensitive attributes and faces dimension problems.The features important to gas-bearing reservoir prediction could not be the main features of the samples.Thus,linear dimension reduction methods,such as principal component analysis,fail to extract relevant features.We thus implemented dimension reduction using a fully connected artifi cial neural network(ANN)with proper architecture.This not only increased the separability of the samples but also maintained the samples’inherent distribution characteristics.Moreover,using the kNN to classify samples after the ANN dimension reduction is also equivalent to replacing the deep structure of the ANN,which is considered to have a linear classifi cation function.When applied to actual data,our method extracted gas-bearing sensitive features from seismic data to a certain extent.The prediction results can characterize gas-bearing reservoirs accurately in a limited scope. 展开更多
关键词 gas bearing prediction interpretability k-nearest neighbor nonlinear directional dimension reduction
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Neighboring optimal guidance using higher-order dynamics approximation with application to orbital injection problem 认领 引用
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作者 Xinglun CHEN Ran ZHANG Huifeng LI 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2024年第5期426-440,共15页
Neighboring optimal guidance,a method to obtain a suboptimal guidance law by approximately solving the first-order necessary conditions based on a nominal trajectory,is widely used in the aerospace field due to its hi... Neighboring optimal guidance,a method to obtain a suboptimal guidance law by approximately solving the first-order necessary conditions based on a nominal trajectory,is widely used in the aerospace field due to its high computational efficiency and low resource usage.For more advanced scenarios,the existing methods still have a problem that the guidance accuracy and optimality will seriously degrade when the actual state largely deviates from the nominal trajectory.This is mainly caused by the approximate description of the first-order conditions in terms of total flight time and nonlinear constraints.To address this problem,a higher-order neighboring optimal guidance method is proposed.First,a novel total flight time updating strategy,together with a normalized time scale,is presented that transforms the optimal problem with free total flight time into a more tractable optimal problem with fixed total flight time.Then,using the vector partial derivative method,a higher-order approximation is adopted,instead of the first-order approximation,to accurately describe the nonlinear dynamical and terminal constraints,thus obtaining a polynomially constrained quadratic optimal problem.Finally,to numerically solve the polynomially constrained quadratic optimal problem,a Newton-type iterative algorithm based on the orthogonal decomposition is designed.Through the iterative solution within each guidance period,the corrections to control quantities and total flight time are generated.The proposed method is applied to a launch vehicle orbital injection problem,and simulation results show that it achieves high accuracy of orbital injection and optimality of performance index. 展开更多
关键词 Optimal control Neighboring optimal guidance Launch vehicles Orbital injection Higher-order approximation
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Mapping aboveground biomass by integrating geospatial and forest inventory data through a k-nearest neighbor strategy in North Central Mexico 认领 引用 被引量：3
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作者 Carlos A AGUIRRE-SALADO Eduardo J TREVIO-GARZA +7 位作者 Oscar A AGUIRRE-CALDERóN Javier JIMNEZ-PREZ Marco A GONZLEZ-TAGLE José R VALDZ-LAZALDE Guillermo SNCHEZ-DíAZ Reija HAAPANEN Alejandro I AGUIRRE-SALADO Liliana MIRANDA-ARAGóN 《Journal of Arid Land》 SCIE CSCD 2014年第1期80-96,共17页
As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring s... As climate change negotiations progress,monitoring biomass and carbon stocks is becoming an important part of the current forest research.Therefore,national governments are interested in developing forest-monitoring strategies using geospatial technology.Among statistical methods for mapping biomass,there is a nonparametric approach called k-nearest neighbor(kNN).We compared four variations of distance metrics of the kNN for the spatially-explicit estimation of aboveground biomass in a portion of the Mexican north border of the intertropical zone.Satellite derived,climatic,and topographic predictor variables were combined with the Mexican National Forest Inventory(NFI)data to accomplish the purpose.Performance of distance metrics applied into the kNN algorithm was evaluated using a cross validation leave-one-out technique.The results indicate that the Most Similar Neighbor(MSN)approach maximizes the correlation between predictor and response variables(r=0.9).Our results are in agreement with those reported in the literature.These findings confirm the predictive potential of the MSN approach for mapping forest variables at pixel level under the policy of Reducing Emission from Deforestation and Forest Degradation(REDD+). 展开更多
关键词 k-nearest neighbor Mahalanobis most similar neighbor MODIS BRDF-adjusted reflectance forest inventory the policy of Reducing Emission from Deforestation and Forest Degradation
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Developed Time-OptimalModel Predictive Static Programming Method with Fish Swarm Optimization for Near-Space Vehicle 认领 引用
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作者 Yuanzhuo Wang Honghua Dai 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第5期1463-1484,共22页
To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the mod... To establish the optimal reference trajectory for a near-space vehicle under free terminal time,a time-optimal model predictive static programming method is proposed with adaptive fish swarm optimization.First,the model predictive static programming method is developed by incorporating neighboring terms and trust region,enabling rapid generation of precise optimal solutions.Next,an adaptive fish swarm optimization technique is employed to identify a sub-optimal solution,while a momentum gradient descent method with learning rate decay ensures the convergence to the global optimal solution.To validate the feasibility and accuracy of the proposed method,a near-space vehicle example is analyzed and simulated during its glide phase.The simulation results demonstrate that the proposed method aligns with theoretical derivations and outperforms existing methods in terms of convergence speed and accuracy.Therefore,the proposed method offers significant practical value for solving the fast trajectory optimization problem in near-space vehicle applications. 展开更多
关键词 Near-space vehicle model predictive static programming neighboring term and trust region optimal control adaptive fish swarm optimization
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Computational Intelligence Prediction Model Integrating Empirical Mode Decomposition,Principal Component Analysis,and Weighted k-Nearest Neighbor 认领 引用 被引量：2
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作者 Li Tang He-Ping Pan Yi-Yong Yao 《Journal of Electronic Science and Technology》 EI CAS CSCD 2020年第4期341-349,共9页
On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feat... On the basis of machine leaning,suitable algorithms can make advanced time series analysis.This paper proposes a complex k-nearest neighbor(KNN)model for predicting financial time series.This model uses a complex feature extraction process integrating a forward rolling empirical mode decomposition(EMD)for financial time series signal analysis and principal component analysis(PCA)for the dimension reduction.The information-rich features are extracted then input to a weighted KNN classifier where the features are weighted with PCA loading.Finally,prediction is generated via regression on the selected nearest neighbors.The structure of the model as a whole is original.The test results on real historical data sets confirm the effectiveness of the models for predicting the Chinese stock index,an individual stock,and the EUR/USD exchange rate. 展开更多
关键词 Empirical mode decomposition(EMD) k-nearest neighbor(KNN) principal component analysis(PCA) time series
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基于不规则区域划分方法的k-Nearest Neighbor查询算法 认领 引用 被引量：1
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作者 张清清 李长云 +3 位作者 李旭 周玲芳 胡淑新 邹豪杰 《计算机系统应用》 2015年第9期186-190,共5页
随着越来越多的数据累积,对数据处理能力和分析能力的要求也越来越高.传统k-Nearest Neighbor(k NN)查询算法由于其容易导致计算负载整体不均衡的规则区域划分方法及其单个进程或单台计算机运行环境的较低数据处理能力.本文提出并详细... 随着越来越多的数据累积,对数据处理能力和分析能力的要求也越来越高.传统k-Nearest Neighbor(k NN)查询算法由于其容易导致计算负载整体不均衡的规则区域划分方法及其单个进程或单台计算机运行环境的较低数据处理能力.本文提出并详细介绍了一种基于不规则区域划分方法的改进型k NN查询算法,并利用对大规模数据集进行分布式并行计算的模型Map Reduce对该算法加以实现.实验结果与分析表明,Map Reduce框架下基于不规则区域划分方法的k NN查询算法可以获得较高的数据处理效率,并可以较好的支持大数据环境下数据的高效查询. 展开更多
关键词 k-Nearest Neighbor(k NN)查询算法 不规则区域划分方法 Map Reduce 大数据
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Active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations 认领 引用
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作者 Zhifeng Xu Jiyin Cao +2 位作者 Gang Zhang Xuyong Chen Yushun Wu 《Defence Technology（防务技术）》 SCIE EI CAS CSCD 2023年第10期306-313,共8页
This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a rand... This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability. 展开更多
关键词 Active learning Monte-carlo simulation K-nearest neighbors Reliability estimation Classification
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Wireless Communication Signal Strength Prediction Method Based on the K-nearest Neighbor Algorithm 认领 引用
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作者 Zhao Chen Ning Xiong +6 位作者 Yujue Wang Yong Ding Hengkui Xiang Chenjun Tang Lingang Liu Xiuqing Zou Decun Luo 《国际计算机前沿大会会议论文集》 EI 2019年第1期238-240,共3页
Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically ... Existing interference protection systems lack automatic evaluation methods to provide scientific, objective and accurate assessment results. To address this issue, this paper develops a layout scheme by geometrically modeling the actual scene, so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex scenes. An improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being shield. Then the highest accuracy set of data could be picked out by comparison. The experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes’ signal strength and evaluate the interference protection with high accuracy. 展开更多
关键词 Interference protection K-nearest neighbor algorithm Non-parametric kernel regression Signal field strength
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Pruned fuzzy K-nearest neighbor classifier for beat classification 认领 引用 被引量：5
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作者 Muhammad Arif Muhammad Usman Akram Fayyaz-ul-Afsar Amir Minhas 《Journal of Biomedical Science and Engineering》 2010年第4期380-389,共10页
Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats... Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data. 展开更多
关键词 Arrhythmia ECG K-Nearest Neighbor Pruning Fuzzy Classification
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A Short-Term Traffic Flow Forecasting Method Based on a Three-Layer K-Nearest Neighbor Non-Parametric Regression Algorithm 认领 引用 被引量：8
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作者 Xiyu Pang Cheng Wang Guolin Huang 《Journal of Transportation Technologies》 2016年第4期200-206,共7页
Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting... Short-term traffic flow is one of the core technologies to realize traffic flow guidance. In this article, in view of the characteristics that the traffic flow changes repeatedly, a short-term traffic flow forecasting method based on a three-layer K-nearest neighbor non-parametric regression algorithm is proposed. Specifically, two screening layers based on shape similarity were introduced in K-nearest neighbor non-parametric regression method, and the forecasting results were output using the weighted averaging on the reciprocal values of the shape similarity distances and the most-similar-point distance adjustment method. According to the experimental results, the proposed algorithm has improved the predictive ability of the traditional K-nearest neighbor non-parametric regression method, and greatly enhanced the accuracy and real-time performance of short-term traffic flow forecasting. 展开更多
关键词 Three-Layer Traffic Flow Forecasting K-Nearest Neighbor Non-Parametric Regression
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bDWPLO-FKNN:A Novel Machine Learning Model for Predicting COVID-19 Severity Using Differential Weibull Polar Lights Optimizer 认领 引用
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作者 Caibing Shang Meifang Huang Sudan Yu 《Journal of Bionic Engineering》 SCIE EI CSCD 2025年第6期3188-3208,共21页
The COVID-19 pandemic,caused by the SARS-CoV-2 virus,has triggered a global health crisis,necessitating accurate predictive models to forecast disease severity and aid in clinical decision-making.This study introduces... The COVID-19 pandemic,caused by the SARS-CoV-2 virus,has triggered a global health crisis,necessitating accurate predictive models to forecast disease severity and aid in clinical decision-making.This study introduces an innovative machine learning approach,the bDWPLO-FKNN model,designed to predict the severity of COVID-19 pneumonia in patients.The model incorporates the Differential Weibull Polar Lights Optimizer(DWPLO),an enhancement of the Polar Lights Optimizer(PLO)with the differential evolution operator and the Weibull flight operator,to perform effective feature selection.The DWPLO’s performance was rigorously tested against IEEE CEC 2017 benchmark functions,demonstrating its robust optimization capabilities.The binary version of DWPLO(bDWPLO)was then integrated with the Fuzzy K-Nearest Neighbors(FKNN)algorithm to form the predictive model.Using a dataset from the People’s Hospital Affiliated with Ningbo University,the model was trained to identify patients at risk of developing severe pneumonia due to COVID-19.The bDWPLO-FKNN model exhibited exceptional predictive accuracy,with an accuracy of 84.036% and a specificity of 88.564%.The analysis revealed key predictors,including albumin,albumin to globulin ratio,lactate dehydrogenase,urea nitrogen,gamma-glutamyl transferase,and inorganic phosphorus,which were significantly associated with disease severity.The integration of DWPLO with FKNN not only enhances feature selection but also bolsters the model’s predictive power,providing a valuable tool for clinicians to assess patient risk and allocate healthcare resources effectively during the COVID-19 pandemic. 展开更多
关键词 COVID-19 severity prediction Polar lights optimizer Differential evolution Weibull flight operator Fuzzy k-nearest neighbors
Short-term Traffic Flow Forecasting Based on Combination of K-nearest Neighbor and Support Vector Regression 认领 引用 被引量：7
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作者 LIU Zhao DU Wei +2 位作者 YAN Dong-mei CHAI Gan GUO Jian-hua 《Journal of Highway and Transportation Research and Development(English Edition)》 2018年第1期89-96,共8页
To improve the forecasting accuracy of short-term traffic flow and provide precise and reliable traffic information for traffic management units and travelers,this study proposes a hybrid prediction model that is base... To improve the forecasting accuracy of short-term traffic flow and provide precise and reliable traffic information for traffic management units and travelers,this study proposes a hybrid prediction model that is based on the characteristics of K-nearest neighbor(KNN)method and support vector regression(SVR).The proposed hybrid model,i.e.KNN-SVR,mimics the search mechanism of the KNN method to reconstruct a time series of historical traffic flow that is similar to the current traffic flow.Then,the SVR is used for short-term traffic flow forecasting.Using actual traffic flow data,we study the effect of the traffic flows on target and adjacent section roads and analyze the forecasting accuracy of the proposed model.Results show that the KNN-SVR model that considers the target and adjacent section roads has the best performance,having a mean absolute percentage error(MAPE)of 8.29%.The forecasting error of the KNN-SVR model that considers only the target section road is slightly large,having an average MAPE of 9.16%.Furthermore,the forecasting accuracy of the KNN-SVR model is better than that of traditional prediction models,such as the KNN method,SVR,and neural networks. 展开更多
关键词 traffic engineering prediction model K-nearest neighbor support vector regression short-term traffic flow
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Face Recognition by Combining Wavelet Transform and k-Nearest Neighbor 认领 引用 被引量：2
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作者 Yugang Jiang Ping Guo 《通讯和计算机（中英文版）》 2005年第9期50-53,共4页
A novel technique for face recognition is presented in this paper. Wavelet transform and k-nearest neighbor rule are combined in this technique. Wavelet transform is adopted to obtain the low pass images of the origin... A novel technique for face recognition is presented in this paper. Wavelet transform and k-nearest neighbor rule are combined in this technique. Wavelet transform is adopted to obtain the low pass images of the original face images in order to reduce the impact of facial expressions. When the scales of the head of an individual in sample images are different, wavelet transform is employed to extract the position of racial features. Then a new method is adopted to construct feature vectors using the grey values of face images. Finally, k-nearest neighbor rule is employed to complete the recognition process. Experimental results show that the proposed technique is efficient at computation and robust to the facial expressions. 展开更多
关键词 Face Recognition k-nearest Neighbor Rule Wavelet Transform Principle Component Analysis
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Propagation Path Loss Models at 28 GHz Using K-Nearest Neighbor Algorithm 认领 引用
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作者 Vu Thanh Quang Dinh Van Linh To Thi Thao 《通讯和计算机（中英文版）》 2022年第1期1-8,共8页
In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based o... In this paper,we develop and apply K-Nearest Neighbor algorithm to propagation pathloss regression.The path loss models present the dependency of attenuation value on distance using machine learning algorithms based on the experimental data.The algorithm is performed by choosing k nearest points and training dataset to find the optimal k value.The proposed method is applied to impove and adjust pathloss model at 28 GHz in Keangnam area,Hanoi,Vietnam.The experiments in both line-of-sight and non-line-of-sight scenarios used many combinations of transmit and receive antennas at different transmit antenna heights and random locations of receive antenna have been carried out using Wireless Insite Software.The results have been compared with 3GPP and NYU Wireless Path Loss Models in order to verify the performance of the proposed approach. 展开更多
关键词 K-nearest neighbor regression 5G millimeter waves path loss
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