期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到9篇文章
< 1 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
An Unsupervised Feature Selection Algorithm with Feature Ranking for Maximizing Performance of the Classifiers 认领 引用 被引量：2
1
作者 Danasingh Asir Antony Gnana Singh Subramanian Appavu Alias Balamurugan Epiphany Jebamalar Leavline 《International Journal of Automation and computing》 CSCD 2015年第5期511-517,共7页
Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is ... Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy,efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical,finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection(FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes(NB),instance based(IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers. 展开更多
关键词 Feature selection algorithm classification cluster
暂未订购 下载PDF
Enhancing Epileptic Seizure Classification via Multi-Feature Fusion in a Transformer-LSTM Architecture 认领 引用
2
作者 Gaoteng Yuan Ping Qiu +3 位作者 Qika Lin Jianchu Lin Xiang Li Dongping Gao 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第6期1134-1154,共21页
Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective interventio... Epilepsy is a chronic neurological disorder characterized by recurrent seizures,posing significant challenges to patients’quality of life.Accurate classification of seizure states is crucial for effective intervention.This paper presents a deep learning-based approach for epileptic seizure classification by integrating multi-feature analysis of electroencephalogram(EEG)signals.The proposed method begins with signal preprocessing,including denoising,segmentation,and label construction.Subsequently,a comprehensive set of temporal,spectral,and wavelet-based features—such as signal mean,power,heart rate,and wavelet coefficients—is extracted.Feature selection is then performed using the Maximal Information Coefficient(MIC)to identify the most discriminative inputs.A hybrid model combining a Transformer encoder and a Long Short-Term Memory(LSTM)network is developed to effectively capture both long-range dependencies and temporal dynamics in EEG sequences for seizure classification.Evaluated on the Bonn dataset using 5-fold cross-validation,the proposed method achieves an accuracy of 96.43%in distinguishing between epileptic patients and healthy subjects,with a sensitivity of 97.53%in detecting seizure states.It also attains a multi-class classification accuracy of 90.14%across different epileptic signal types.Ablation studies confirm that MICbased feature selection improves accuracy by over 2O%compared to using raw features without selection.The results demonstrate that the integration of multi-feature analysis with the Transformer-LSTM architecture offers an effective and reliable solution for EEG-based seizure classification. 展开更多
关键词 Epileptic classification EEG signal wavelet analysis feature selection transformer-LSTM Algorithm 2:MIC algorithm feature selection
暂未订购 下载PDF
Boosted Spider Wasp Optimizer for High-dimensional Feature Selection 认领 引用
3
作者 Elfadil A.Mohamed Malik Sh.Braik +1 位作者 Mohammed Azmi Al-Betar Mohammed A.Awadallah 《Journal of Bionic Engineering》 SCIE EI CSCD 2024年第5期2424-2459,共36页
With the increasing dimensionality of the data,High-dimensional Feature Selection(HFS)becomes an increasingly dif-ficult task.It is not simple to find the best subset of features due to the breadth of the search space... With the increasing dimensionality of the data,High-dimensional Feature Selection(HFS)becomes an increasingly dif-ficult task.It is not simple to find the best subset of features due to the breadth of the search space and the intricacy of the interactions between features.Many of the Feature Selection(FS)approaches now in use for these problems perform sig-nificantly less well when faced with such intricate situations involving high-dimensional search spaces.It is demonstrated that meta-heuristic algorithms can provide sub-optimal results in an acceptable amount of time.This paper presents a new binary Boosted version of the Spider Wasp Optimizer(BSWO)called Binary Boosted SWO(BBSWO),which combines a number of successful and promising strategies,in order to deal with HFS.The shortcomings of the original BSWO,including early convergence,settling into local optimums,limited exploration and exploitation,and lack of population diversity,were addressed by the proposal of this new variant of SWO.The concept of chaos optimization is introduced in BSWO,where initialization is consistently produced by utilizing the properties of sine chaos mapping.A new convergence parameter was then incorporated into BSWO to achieve a promising balance between exploration and exploitation.Multiple exploration mechanisms were then applied in conjunction with several exploitation strategies to effectively enrich the search process of BSWO within the search space.Finally,quantum-based optimization was added to enhance the diversity of the search agents in BSWO.The proposed BBSWO not only offers the most suitable subset of features located,but it also lessens the data's redundancy structure.BBSWO was evaluated using the k-Nearest Neighbor(k-NN)classifier on 23 HFS problems from the biomedical domain taken from the UCI repository.The results were compared with those of traditional BSWO and other well-known meta-heuristics-based FS.The findings indicate that,in comparison to other competing techniques,the proposed BBSWO can,on average,identify the least significant subsets of features with efficient classification accuracy of the k-NN classifier. 展开更多
关键词 High-dimensional features SWO algorithm Feature selection Optimization Machine learning
A zoning-based machine learning framework for accurate soil organic matter prediction across Mollisol and non-Mollisol regions 认领 引用
4
作者 Xue Li Bo Jiang +5 位作者 Depiao Kong Deqiang Zang Ya Chen Changkun Wang Huanjun Liu Chong Luo 《Journal of Integrative Agriculture》 SCIE CAS CSCD 2026年第8期3453-3468,共16页
Soil organic matter(SOM)is a core indicator of soil fertility and ecosystem function.However,in regions where Mollisol and non-Mollisol coexist,high-precision spatial mapping faces significant challenges due to pronou... Soil organic matter(SOM)is a core indicator of soil fertility and ecosystem function.However,in regions where Mollisol and non-Mollisol coexist,high-precision spatial mapping faces significant challenges due to pronounced terrain heterogeneity and redundancy in high-dimensional covariates.This study proposes a"remote sensing zoning-feature selection optimization-random forest(RSZ-FSO-RF)"framework.By integrating Landsat-8 multi-temporal imagery from 2014-2023 with topographic and climatic factors,and leveraging the Google Earth Engine(GEE)platform,it achieves highprecision remote sensing zoning of Mollisol and non-Mollisol areas(overall accuracy:92.13%,Kappa coefficient:0.70).Subsequently,local Random Forest(RF)regression models were established within each zone for SOM prediction,with predictive variables optimized using recursive feature elimination(RFE).Results demonstrate that compared to FAOzone-based modeling,the RSZ-FSO-RF framework significantly enhances prediction accuracy(R2=0.619,RMSE=6.849 g kg-1).And further feature optimization continued to enhance model performance(R2=0.627,RMSE=6.781 g kg-1).Notably,optimal predictor combinations varied significantly across zones,with SOM spatial variability generally higher in non-Mollisol areas than in Mollisol regions.By organically integrating remote sensing zoning with feature selection,this framework effectively mitigates covariate redundancy while accounting for local heterogeneity,significantly enhancing the accuracy and stability of high-resolution SOM mapping.Furthermore,this study provides scientific basis and decision support for soil resource management and sustainable agricultural development under complex topographic conditions. 展开更多
关键词 soil organic matter remote sensing zoning feature selection algorithm random forest algorithm prediction
暂未订购 下载PDF
Back Propagation Neural Network model for analysis of hyperspectral images to predict apple firmness 认领 引用
5
作者 Shuiping Li Yueyue Chen +5 位作者 Xiaobo Zhang Junbo Wang Xuanxiang Gao Yunhong Jiang Zhaojun Ban Cunkun Chen 《Food Innovation and Advances》 2025年第1期1-9,共9页
The potential of employing hyperspectral imaging(HSl)in the near-infrared(NiR)range(386.82-1,004.50 nm)for predicting the firmness of'Fuji'apples cultivated in Aksu has been evaluated.The performance of seven ... The potential of employing hyperspectral imaging(HSl)in the near-infrared(NiR)range(386.82-1,004.50 nm)for predicting the firmness of'Fuji'apples cultivated in Aksu has been evaluated.The performance of seven preprocessing algorithms and two feature selection algorithms was evaluated.The coefficient of determination(R2)and root mean square error(RMsE)of Partial Least Squares(PLS)models are contrasted using various inputs.These results confirm that the Multiplicative Scatter Correction(MSC)preprocessing algorithm was the optimal choice(R2p=0.7925,RMSEP=0.6537),and the Competitive Adaptive Reweighted Sampling(CARS)feature selection algorithm demonstrated superior performance(R2p=0.8325,RMSEP=0.6257).Based on the aforementioned findings,PLS,Multiple Linear Regression(MLR),Heterogeneous Transfer Learning(HTL),and Back Propagation Neural Network(BPNN)models were constructed for cross-validation purposes.The experimental results indicate that the CARS-BPNN model exhibits the optimal prediction performance,with an R2pvalue of 0.9350 and an RMSEP value of 0.4654.The results of the research indicated that a deep learning method combined with hyperspectral imaging technology could be utilized to non-destructively detect the firmness of'Fuji'apples,which will be beneficial and potentially applicable for post-harvest fruit firmness monitoring.This research provides a reference point for the non-destructive detection of apple in the selection of preprocessing,feature selection algorithms,and predicting firmness model. 展开更多
关键词 predicting firmness feature selection algorithms Fujiapples root mean square hyperspectral imaging hsl preprocessing algorithms coefficient determination r hyperspectral imaging
暂未订购 下载PDF
Apple leaf disease identification using genetic algorithm and correlation based feature selection method 认领 引用 被引量：27
6
作者 Zhang Chuanlei Zhang Shanwen +2 位作者 Yang Jucheng Shi Yancui Chen Jia 《International Journal of Agricultural and Biological Engineering》 SCIE 2017年第2期74-83,共10页
Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best tim... Apple leaf disease is one of the main factors to constrain the apple production and quality.It takes a long time to detect the diseases by using the traditional diagnostic approach,thus farmers often miss the best time to prevent and treat the diseases.Apple leaf disease recognition based on leaf image is an essential research topic in the field of computer vision,where the key task is to find an effective way to represent the diseased leaf images.In this research,based on image processing techniques and pattern recognition methods,an apple leaf disease recognition method was proposed.A color transformation structure for the input RGB(Red,Green and Blue)image was designed firstly and then RGB model was converted to HSI(Hue,Saturation and Intensity),YUV and gray models.The background was removed based on a specific threshold value,and then the disease spot image was segmented with region growing algorithm(RGA).Thirty-eight classifying features of color,texture and shape were extracted from each spot image.To reduce the dimensionality of the feature space and improve the accuracy of the apple leaf disease identification,the most valuable features were selected by combining genetic algorithm(GA)and correlation based feature selection(CFS).Finally,the diseases were recognized by SVM classifier.In the proposed method,the selected feature subset was globally optimum.The experimental results of more than 90%correct identification rate on the apple diseased leaf image database which contains 90 disease images for there kinds of apple leaf diseases,powdery mildew,mosaic and rust,demonstrate that the proposed method is feasible and effective. 展开更多
关键词 apple leaf disease diseased leaf recognition region growing algorithm(RGA) genetic algorithm and correlation based feature selection(GA-CFS)
森林优化特征选择算法的增强与扩展 认领 引用 被引量：10
7
作者 刘兆赓 李占山 +2 位作者 王丽 王涛 于海鸿 《软件学报》 EI CSCD 北大核心 2020年第5期1511-1524,共14页
特征选择作为一种重要的数据预处理方法,不但能解决维数灾难问题,还能提高算法的泛化能力.各种各样的方法已被应用于解决特征选择问题,其中,基于演化计算的特征选择算法近年来获得了更多的关注并取得了一些成功.近期研究结果表明,森林... 特征选择作为一种重要的数据预处理方法,不但能解决维数灾难问题,还能提高算法的泛化能力.各种各样的方法已被应用于解决特征选择问题,其中,基于演化计算的特征选择算法近年来获得了更多的关注并取得了一些成功.近期研究结果表明,森林优化特征选择算法具有更好的分类性能及维度缩减能力.然而,初始化阶段的随机性、全局播种阶段的人为参数设定,影响了该算法的准确率和维度缩减能力;同时,算法本身存在着高维数据处理能力不足的本质缺陷.从信息增益率的角度给出了一种初始化策略,在全局播种阶段,借用模拟退火控温函数的思想自动生成参数,并结合维度缩减率给出了适应度函数;同时,针对形成的优质森林采取贪心算法,形成一种特征选择算法EFSFOA(enhanced feature selection using forest optimization algorithm).此外,在面对高维数据的处理时,采用集成特征选择的方案形成了一个适用于EFSFOA的集成特征选择框架,使其能够有效处理高维数据特征选择问题.通过设计对比实验,验证了EFSFOA与FSFOA相比在分类准确率和维度缩减率上均有明显的提高,高维数据处理能力更是提高到了100 000维.将EFSFOA与近年来提出的比较高效的基于演化计算的特征选择方法进行对比,EFSFOA仍具有很强的竞争力. 展开更多
关键词 enhanced feature selection using forest optimization algorithm(EFSFOA) 高维 特征选择 演化计算
暂未订购 下载PDF
A Feature Selection Method for Prediction Essential Protein 认领 引用 被引量：8
8
作者 Jiancheng Zhong Jianxin Wang +2 位作者 Wei Peng Zhen Zhang Min Li 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2015年第5期491-499,共9页
Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed t... Essential proteins are vital to the survival of a cell. There are various features related to the essentiality of proteins, such as biological and topological features. Many computational methods have been developed to identify essential proteins by using these features. However, it is still a big challenge to design an effective method that is able to select suitable features and integrate them to predict essential proteins. In this work, we first collect 26 features, and use SVM-RFE to select some of them to create a feature space for predicting essential proteins, and then remove the features that share the biological meaning with other features in the feature space according to their Pearson Correlation Coefficients（PCC）. The experiments are carried out on S. cerevisiae data. Six features are determined as the best subset of features. To assess the prediction performance of our method, we further compare it with some machine learning methods, such as SVM, Naive Bayes, Bayes Network, and NBTree when inputting the different number of features. The results show that those methods using the 6 features outperform that using other features, which confirms the effectiveness of our feature selection method for essential protein prediction. 展开更多
关键词 essential protein feature selection Protein-Protein Interaction（PPI） machine learning centrality algorithm
暂未订购 下载PDF
BFS-SVM Classifier for QoS and Resource Allocation in Cloud Environment 认领 引用
9
作者 A.Richard William J.Senthilkumar +1 位作者 Y.Suresh V.Mohanraj 《Computer Systems Science & Engineering》 SCIE EI 2023年第10期777-790,共14页
In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocatio... In cloud computing Resource allocation is a very complex task.Handling the customer demand makes the challenges of on-demand resource allocation.Many challenges are faced by conventional methods for resource allocation in order tomeet the Quality of Service(QoS)requirements of users.For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work.The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with feature selection(BFS)in the proposed work,this further reduces the inappropriate features from the data.The similarities that were hidden can be demoralized by the Support Vector Machine(SVM)classifier which is also determine the subspace vector and then a new feature vector can be predicted by using SVM.For an unexpected circumstance SVM model can make a resource allocation decision.The efficiency of proposed SVM classifier of resource allocation can be highlighted by using a singlecell multiuser massive Multiple-Input Multiple Output(MIMO)system,with beam allocation problem as an example.The proposed resource allocation based on SVM performs efficiently than the existing conventional methods;this has been proven by analysing its results. 展开更多
关键词 Bat algorithm with feature selection(BFS) support vector machine(SVM) multiple-input multiple output(MIMO) quality of service(QoS) classifier cloud computing
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈