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Unsupervised Quick Reduct Algorithm Using Rough Set Theory 认领 引用 被引量：2
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作者 C. Velayutham K. Thangavel 《Journal of Electronic Science and Technology》 CAS 2011年第3期193-201,共9页
Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features ma... Feature selection （FS） is a process to select features which are more informative. It is one of the important steps in knowledge discovery. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. The conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. In this paper, we propose a new unsupervised quick reduct （QR） algorithm using rough set theory. The quality of the reduced data is measured by the classification performance and it is evaluated using WEKA classifier tool. The method is compared with existing supervised methods and the result demonstrates the efficiency of the proposed algorithm. 展开更多
关键词 Index Terms--Data mining rough set supervised and unsupervised feature selection unsupervised quick reduct algorithm.
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Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements 认领 引用 被引量：1
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作者 Haiwen Chen Guang Yu +5 位作者 Fang Liu Zhiping Cai Anfeng Liu Shuhui Chen Hongbin Huang Chak Fong Cheang 《Computers, Materials & Continua》 SCIE EI 2020年第2期917-927,共11页
For many Internet companies,a huge amount of KPIs(e.g.,server CPU usage,network usage,business monitoring data)will be generated every day.How to closely monitor various KPIs,and then quickly and accurately detect ano... For many Internet companies,a huge amount of KPIs(e.g.,server CPU usage,network usage,business monitoring data)will be generated every day.How to closely monitor various KPIs,and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge,especially for unlabeled data.The generated KPIs can be detected by supervised learning with labeled data,but the current problem is that most KPIs are unlabeled.That is a time-consuming and laborious work to label anomaly for company engineers.Build an unsupervised model to detect unlabeled data is an urgent need at present.In this paper,unsupervised learning DBSCAN combined with feature extraction of data has been used,and for some KPIs,its best F-Score can reach about 0.9,which is quite good for solving the current problem. 展开更多
关键词 Anomaly detection KPIs unsupervised learning algorithm
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A Comparative Study of Image Classification Algorithms for Landscape Assessment of the Niger Delta Region 认领 引用
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作者 Omoleomo Olutoyin Omo-Irabor 《Journal of Geographic Information System》 2016年第2期163-170,共8页
A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and... A critical problem associated with the southern part of Nigeria is the rapid alteration of the landscape as a result of logging, agricultural practices, human migration and expansion, oil exploration, exploitation and production activities. These processes have had both positive and negative effects on the economic and socio-political development of the country in general. The negative impacts have led not only to the degradation of the ecosystem but also posing hazards to human health and polluting surface and ground water resources. This has created the need for the development of a rapid, cost effective and efficient land use/land cover (LULC) classification technique to monitor the biophysical dynamics in the region. Due to the complex land cover patterns existing in the study area and the occasionally indistinguishable relationship between land cover and spectral signals, this paper introduces a combined use of unsupervised and supervised image classification for detecting land use/land cover (LULC) classes. With the continuous conflict over the impact of oil activities in the area, this work provides a procedure for detecting LULC change, which is an important factor to consider in the design of an environmental decision-making framework. Results from the use of this technique on Landsat TM and ETM+ of 1987 and 2002 are discussed. The results reveal the pros and cons of the two methods and the effects of their overall accuracy on post-classification change detection. 展开更多
关键词 Land Cover Supervised and Unsupervised Classification Algorithms Landsat Images Change Detection Niger Delta
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Development of an unsupervised 3D LiDAR-based methodology for automated safety monitoring of railway facilities 认领 引用
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作者 Ehsan Nateghinia Luis F.Miranda-Moreno 《International Journal of Transportation Science and Technology》 EI CSCD 2026年第1期157-172,共16页
Railway safety(e.g.,at grade crossings,platforms,or rail tracks)is a primary concern for transportation authorities.Unfortunately,preventable railway collisions claim the lives of hundreds annually,often involving ind... Railway safety(e.g.,at grade crossings,platforms,or rail tracks)is a primary concern for transportation authorities.Unfortunately,preventable railway collisions claim the lives of hundreds annually,often involving individuals crossing illegally at highway-railway grade crossings or trespassing at unauthorized railroad facilities.Transportation authorities often deploy a range of engineering countermeasures to mitigate the frequency or risk of such events.These countermeasures include technological solutions that automatically activate warning systems,barriers,or gates to alert and deter road users from unlawfully entering restricted railway facilities.For the safety monitoring of such facilities,alternative sensing technologies such as video-based computer-vision systems have been evaluated and,in some cases,utilized in practice.Despite their merits,automated LiDAR-based detection and tracking methods have yet to be explored in railway safety applications.This research aims to introduce and assess an unsupervised 3D-LiDAR-based methodology for monitoring rail-road level facilities.This study’s core is the implementation of an unsupervised learning algorithm designed to detect,track,and classify road users using point clouds gathered by a 3D-LiDAR sensor.The proposed methodology demonstrates encouraging results when monitoring rail-road level crossings.The aggregate average absolute percentage deviation(AAPD)for motorized road users and counting motorized road users stands at 5%and 3%,for non-motorized road users at 10%and 14%on two separate test days,each featuring distinct system installations. 展开更多
关键词 3D LiDAR sensor Level crossing monitoring Trespassing detection Unsupervised LiDAR algorithm Alternative technologies
Unsupervised identification of crystal defects from atomistic potential descriptors 认领 引用 被引量：1
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作者 LukášKývala Pablo Montero de Hijes Christoph Dellago 《npj Computational Materials》 SCIE EI CSCD 2025年第1期509-516,共8页
Identifying crystal defects is vital for unraveling the origins of many physical phenomena.Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for longmolecular d... Identifying crystal defects is vital for unraveling the origins of many physical phenomena.Traditionally used order parameters are system-dependent and can be computationally expensive to calculate for longmolecular dynamics simulations.Unsupervised algorithms offer an alternative independent of the studied system and can utilize precalculated atomistic potential descriptors from molecular dynamics simulations.We compare the performance of three such algorithms(PCA,UMAP,and PaCMAP)on silicon and water systems.Initially,we evaluate the algorithms for recognizing phases,including crystal polymorphs and the melt,followed by an extension of our analysis to identify interstitials,vacancies,and interfaces.While PCA is found unsuitable for effective classification,it has been shown to be a suitable initialization for UMAP and PaCMAP.Both UMAP and PaCMAP show promising results overall,with PaCMAP proving more robust in classification,except in cases of significant class imbalance,where UMAP performs better.Notably,both algorithms successfully identify nuclei in supercooled water,demonstrating their applicability to ice nucleation in water. 展开更多
关键词 identifying crystal defects unraveling origins many physical phenomenatraditionally PCA longmolecular dynamics simulationsunsupervised algorithms precalculated atomistic potential descriptors silicon water systems unsupervised algorithms molecular dynamics simulationswe
WORD SENSE DISAMBIGUATION BASED ON IMPROVED BAYESIAN CLASSIFIERS 认领 引用 被引量：1
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作者 Liu Ting Lu Zhimao Li Sheng 《Journal of Electronics(China)》 2006年第3期394-398,共5页
Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in prac... Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%. 展开更多
关键词 Word Sense Disambiguation （WSD） Natural Language Processing （NLP） Unsupervised learning algorithm Dependency Grammar （DG） Bayesian classifier
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Spectral clustering based on matrix perturbation theory 认领 引用 被引量：22
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作者 TIAN Zheng LI XiaoBin JU YanWei 《Science in China(Series F)》 2007年第1期63-81,共19页
This paper exposes some intrlnsic chsracterlstlca of the spectral clustering method by using the tools from the mstrlx perturbation theory. We construct s welght mstrix of s graph and study Its elgenvalues and elgenve... This paper exposes some intrlnsic chsracterlstlca of the spectral clustering method by using the tools from the mstrlx perturbation theory. We construct s welght mstrix of s graph and study Its elgenvalues and elgenvectors. It shows that the number of clusters Is equal to the number of elgenvslues that are larger than 1, and the number of polnts In each of the clusters can be spproxlmsted by the associated elgenvslue. It also shows that the elgenvector of the weight rnatrlx can be used dlrectly to perform clusterlng; that Is, the dlrectlonsl angle between the two-row vectors of the mstrlx derlved from the elgenvectors Is s sultable distance measure for clustsrlng. As s result, an unsupervised spectral clusterlng slgorlthm based on welght mstrlx （USCAWM） Is developed. The experlmental results on s number of srtlficisl and real-world data sets show the correctness of the theoretical analysis. 展开更多
关键词 spectral clustering weight matrix spectrum of weight matrix number of the clusters unsupervised spectral clustering algorithm based on weight matrix
Neuro-heuristic computational intelligence for solving nonlinear pantograph systems 认领 引用 被引量：2
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作者 Muhammad Asif Zahoor RAJA Iftikhar AHMAD +2 位作者 Imtiaz KHAN Muhammed Ibrahem SYAM Abdul Majid WAZWAZ 《Frontiers of Information Technology & Electronic Engineering》 SCIE EI CSCD 2017年第4期464-484,共21页
We present a neuro-heuristic computing platform for finding the solution for initial value problems （IVPs） of non- linear pantograph systems based on functional differential equations （P-FDEs） of different orders.... We present a neuro-heuristic computing platform for finding the solution for initial value problems （IVPs） of non- linear pantograph systems based on functional differential equations （P-FDEs） of different orders. In this scheme, the strengths of feed-forward artificial neural networks （ANNs）, the evolutionary computing technique mainly based on genetic algorithms （GAs）, and the interior-point technique （IPT） are exploited. Two types of mathematical models of the systems are constructed with the help of ANNs by defining an unsupervised error with and without exactly satisfying the initial conditions. The design parameters of ANN models are optimized with a hybrid approach GA-IPT, where GA is used as a tool for effective global search, and IPT is incorporated for rapid local convergence. The proposed scheme is tested on three different types oflVPs of P-FDE with orders 1-3 The correctness of the scheme is established by comparison with the existing exact solutions. The accuracy and convergence ofthc proposed scheme are further validated through a large number of numerical experiments by taking different numbers of neurons in ANN models. 展开更多
关键词 Neural networks Initial value problems （IVPs） Functional differential equations （FDEs） Unsupervised learning Genetic algorithms （GAs） Interior-point technique （IPT）
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