A high-dimensional classification approach based on class-dependent feature subspace
Abstract
Purpose
The purpose of this paper is to build a compact and accurate classifier for high-dimensional classification.
Design/methodology/approach
A classification approach based on class-dependent feature subspace (CFS) is proposed. CFS is a class-dependent integration of a support vector machine (SVM) classifier and associated discriminative features. For each class, our genetic algorithm (GA)-based approach evolves the best subset of discriminative features and SVM classifier simultaneously. To guarantee convergence and efficiency, the authors customize the GA in terms of encoding strategy, fitness evaluation, and genetic operators.
Findings
Experimental studies demonstrated that the proposed CFS-based approach is superior to other state-of-the-art classification algorithms on UCI data sets in terms of both concise interpretation and predictive power for high-dimensional data.
Research limitations/implications
UCI data sets rather than real industrial data are used to evaluate the proposed approach. In addition, only single-label classification is addressed in the study.
Practical implications
The proposed method not only constructs an accurate classification model but also obtains a compact combination of discriminative features. It is helpful for business makers to get a concise understanding of the high-dimensional data.
Originality/value
The authors propose a compact and effective classification approach for high-dimensional data. Instead of the same feature subset for all the classes, the proposed CFS-based approach obtains the optimal subset of discriminative feature and SVM classifier for each class. The proposed approach enhances both interpretability and predictive power for high-dimensional data.
Keywords
Acknowledgements
The work was supported by the General Program of the National Natural Science Foundation of China (Nos 71771169, 71101103 and 71201115), the State Key Program of National Natural Science of China (No. 71631003) and the program of exchanging scholar supported by the China Scholarship Council (CSC). The authors sincerely appreciate Dongfang Chen, who used to be a Master Student in College of Management and Economics in Tianjin University, to provide preliminary experimental validation in this study.
Citation
Chen, F., Wu, H., Dou, R. and Li, M. (2017), "A high-dimensional classification approach based on class-dependent feature subspace", Industrial Management & Data Systems, Vol. 117 No. 10, pp. 2325-2339. https://doi.org/10.1108/IMDS-11-2016-0491
Publisher
:Emerald Publishing Limited
Copyright © 2017, Emerald Publishing Limited