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Swarm search methods in weka for data mining
Simon Fong1; Robert P. Biuk-Aghai1; Richard C. Millham2
2018-02-26
Conference NameICMLC 2018: 2018 10th International Conference on Machine Learning and Computing
Source PublicationICMLC 2018: Proceedings of the 2018 10th International Conference on Machine Learning and Computing
Pages122-127
Conference Date26 February, 2018- 28 February, 2018
Conference PlaceMacau China
PublisherASSOC COMPUTING MACHINERY, 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
Abstract

Building a good prediction from high-dimensional data model in data mining is a challenging endeavor. One key step in data preprocessing is feature selection (FS) which is about finding the right feature subset for effective supervised learning. FS has two parts: feature evaluators and search methods to find the appropriate features in the search space. In this paper we introduce a collection of search methods that implement metaheuristics search which is also known as swarm search (SS). SS has the advantage over conventional search such as local search, that SS has the facility to explore global optima by a group of autonomous search agents. We have recently added nine new methods to the Weka machine learning workbench. The objective of these nine swarm search methods is to supplement the existing search methods in Weka for providing efficient and effect ive FS in data mining. We have carried out two experiments using synthetic data and medical data. The results show that in general SS has certain advantages over the conventional search methods. The SS methods can be found in the Weka Package Manager as open source code. Researchers and Weka users are encouraged to enhance data mining performance using these free swarm search programs.

KeywordData Mining Search Methods Feature Selection Metaheuristics
DOI10.1145/3195106.3195167
URLView the original
Indexed ByCPCI-S
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000458148400024
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Cited Times [WOS]:15   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorSimon Fong
Affiliation1.Data Analytics and Collaborative Computing Laboratory Department of Computer and Information Science University of Macau, Taipa, Macau SAR
2.ICT and Society Research Group Department of Information Technology Durban University of Technology, Durban, South Africa
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Robert P. Biuk-Aghai,Richard C. Millham. Swarm search methods in weka for data mining[C]:ASSOC COMPUTING MACHINERY, 1515 BROADWAY, NEW YORK, NY 10036-9998 USA,2018:122-127.
APA Simon Fong,Robert P. Biuk-Aghai,&Richard C. Millham.(2018).Swarm search methods in weka for data mining.ICMLC 2018: Proceedings of the 2018 10th International Conference on Machine Learning and Computing,122-127.
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