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Journal of Zhejiang University SCIENCE C 1998 Vol.-1 No.-1 P.

http://doi.org/10.1631/FITEE.2100569


Feature-selection techniques for microarray dataset: a comprehensive review, taxonomy, and future directions


Author(s):  K. BALAKRISHNAN, R. DHANALAKSHMI

Affiliation(s):  Department of Computer Science and Engineering, Indian Institute of Information Technology Tiruchirappalli 620012, India

Corresponding email(s):   bala.k.btech@gmail.com, r_dhanalakshmi@yahoo.com

Key Words:  Feature selection, High-dimensionality, Learning techniques, Microarray dataset


K. BALAKRISHNAN, R. DHANALAKSHMI. Feature-selection techniques for microarray dataset: a comprehensive review, taxonomy, and future directions[J]. Frontiers of Information Technology & Electronic Engineering, 1998, -1(-1): .

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Abstract: 
For optimal results, retrieving a relevant feature from a microarray dataset has become a research focus for researchers involved in the study of feature-selection (FS) techniques. The core aim of this review is to provide a thorough description of various, recent FS techniques. This research critique also focuses on techniques proposed for microarray datasets to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms. We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on microarray datasets. An analysis of existing literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques. A case study is provided to demonstrate the process of implementation in which three microarray cancer datasets were utilized to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.

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