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Feature selection for high dimensional imbalanced class data using harmony search
journal contribution
posted on 2017-01-01, 00:00 authored by Alireza Moayedikia, K L Ong, Y L Boo, William YeohWilliam Yeoh, R JensenMisclassification costs of minority class data in real-world applications can be very high. This is a challenging problem especially when the data is also high in dimensionality because of the increase in overfitting and lower model interpretability. Feature selection is recently a popular way to address this problem by identifying features that best predict a minority class. This paper introduces a novel feature selection method call SYMON which uses symmetrical uncertainty and harmony search. Unlike existing methods, SYMON uses symmetrical uncertainty to weigh features with respect to their dependency to class labels. This helps to identify powerful features in retrieving the least frequent class labels. SYMON also uses harmony search to formulate the feature selection phase as an optimisation problem to select the best possible combination of features. The proposed algorithm is able to deal with situations where a set of features have the same weight, by incorporating two vector tuning operations embedded in the harmony search process. In this paper, SYMON is compared against various benchmark feature selection algorithms that were developed to address the same issue. Our empirical evaluation on different micro-array data sets using G-Mean and AUC measures confirm that SYMON is a comparable or a better solution to current benchmarks.
History
Journal
Engineering applications of artificial intelligenceVolume
57Pagination
38 - 49Publisher
ElsevierLocation
Amsterdam, The NetherlandsPublisher DOI
ISSN
0952-1976Language
engPublication classification
C1.1 Refereed article in a scholarly journal; C Journal articleCopyright notice
2016, ElsevierUsage metrics
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No categories selectedKeywords
Feature selectionHarmony searchHigh-dimensionalityImbalanced classSymmetrical uncertaintyScience & TechnologyTechnologyAutomation & Control SystemsComputer Science, Artificial IntelligenceEngineering, MultidisciplinaryEngineering, Electrical & ElectronicComputer ScienceEngineeringBEE COLONY ALGORITHMCLASSIFICATIONOPTIMIZATION
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