A feature selection technique for generation of classification committees and its application to categorization of laryngeal images

• Published in: Pattern recognition 2009-05, Vol.42 (5), p.645-654
• Main Authors: Bacauskiene, M., Verikas, A., Gelzinis, A., Valincius, D.
• Format: Article
• Language: English
• Subjects: Accuracy; Applied sciences; Classification committee; Classifiers; Evolutionary ensembles; Exact sciences and technology; Feature selection; Feature subset-selection; Feedfoward neural networks; Genetic algorithms; Genetic search; Information technology; Information, signal and communications theory; Informationsteknik; Laryngeal image; Miscellaneous; Negative correlation; Recognition; Signal and communications theory; Signal processing; Signal representation. Spectral analysis; Signal, noise; Support vector machine; Systems; TECHNOLOGY; TEKNIKVETENSKAP; Telecommunications and information theory; Variable selection
• ISSN: 0031-3203; 1873-5142; 1873-5142
• DOI: 10.1016/j.patcog.2008.08.025

This paper is concerned with a two phase procedure to select salient features (variables) for classification committees. Both filter and wrapper approaches to feature selection are combined in this work. In the first phase, definitely redundant features are eliminated based on the paired t-test. The test compares the saliency of the candidate and the noise features. In the second phase, the genetic search is employed. The search integrates the steps of training, aggregation of committee members, selection of hyper-parameters, and selection of salient features into the same learning process. A small number of genetic iterations needed to find a solution is the characteristic feature of the genetic search procedure developed. The experimental tests performed on five real-world problems have shown that significant improvements in classification accuracy can be obtained in a small number of iterations if compared to the case of using all the features available.