Abstract
Abstract Background Feature selection techniques use a search-criteria driven approach for ranked feature subset selection. Often, selecting an optimal subset of ranked features using the existing methods is intractable for high dimensional gene data classification problems. Methods In this paper, an approach based on the individual ability of the features to discriminate between different classes is proposed. The area of overlap measure between feature to feature inter-class and intra-class distance distributions is used to measure the discriminatory ability of each feature. Features with area of overlap below a specified threshold is selected to form the subset. Results The reported method achieves higher classification accuracies with fewer numbers of features for high-dimensional micro-array gene classification problems. Experiments done on CLL-SUB-111, SMK-CAN-187, GLI-85, GLA-BRA-180 and TOX-171 databases resulted in an accuracy of 74.9±2.6, 71.2±1.7, 88.3±2.9, 68.4±5.1, and 69.6±4.4, with the corresponding selected number of features being 1, 1, 3, 37, and 89 respectively. Conclusions The area of overlap between the inter-class and intra-class distances is demonstrated as a useful technique for selection of most discriminative ranked features. Improved classification accuracy is obtained by relevant selection of most discriminative features using the proposed method.
Highlights
Feature selection techniques use a search-criteria driven approach for ranked feature subset selection
High dimensional feature vectors that result from these samples often contain intra-class natural variability reflected as noise and irrelevant information [11,12]
The role of feature selection methods in a high dimensional pattern classification problem is to select the minimum number of features that maximize the recognition accuracy
Summary
An approach based on the individual ability of the features to discriminate between different classes is proposed. The area of overlap measure between feature to feature inter-class and intra-class distance distributions is used to measure the discriminatory ability of each feature. Features with area of overlap below a specified threshold is selected to form the subset
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