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Modified binary bat algorithm for feature selection in unsupervised learning
Joint Authors
Ramasamy, Rajalaxmi
Rani, Sylvia
Source
The International Arab Journal of Information Technology
Issue
Vol. 15, Issue 6 (30 Nov. 2018)8 p.
Publisher
Publication Date
2018-11-30
Country of Publication
Jordan
No. of Pages
8
Main Subjects
Information Technology and Computer Science
Abstract EN
Feature selection is the process of selecting a subset of optimal features by removing redundant and irrelevant features.
In supervised learning, feature selection process uses class label.
But feature selection is difficult in unsupervised learning since class labels are not present.
In this paper, we present a wrapper based unsupervised feature selection method with the modified binary bat approach with k-means clustering algorithm.
To ensure diversification in the search space, mutation operator is introduced in the proposed algorithm.
To validate the selected features by our method, classification algorithms like decision tree induction, Support Vector Machine and Naïve Bayesian classifier are used.
The results show that the proposed method identifies a minimal number of features with improved accuracy when compared with the other methods
American Psychological Association (APA)
Ramasamy, Rajalaxmi& Rani, Sylvia. 2018. Modified binary bat algorithm for feature selection in unsupervised learning. The International Arab Journal of Information Technology،Vol. 15, no. 6.
https://search.emarefa.net/detail/BIM-873974
Modern Language Association (MLA)
Ramasamy, Rajalaxmi& Rani, Sylvia. Modified binary bat algorithm for feature selection in unsupervised learning. The International Arab Journal of Information Technology Vol. 15, no. 6 (Nov. 2018).
https://search.emarefa.net/detail/BIM-873974
American Medical Association (AMA)
Ramasamy, Rajalaxmi& Rani, Sylvia. Modified binary bat algorithm for feature selection in unsupervised learning. The International Arab Journal of Information Technology. 2018. Vol. 15, no. 6.
https://search.emarefa.net/detail/BIM-873974
Data Type
Journal Articles
Language
English
Notes
Includes appendix.
Record ID
BIM-873974