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Exclusive Lasso Assisted Two-Stage Fuzzy-Rough Feature Selection
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  • Yanpeng Qu ,
  • Lin Qiu ,
  • Changjing Shang ,
  • Qiang Shen
Yanpeng Qu
Dalian Maritime University

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Changjing Shang
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Qiang Shen
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Fuzzy-rough sets (FRS) encapsulate the related but distinct concepts of vagueness (for fuzzy sets) and indiscernibility (for rough sets), both of which occur as a result of uncertainty in data, information or knowledge. The application of FRS in feature selection (FS) has employed the dependency degree to guide the FS process with much success. Whilst promising, most existing fuzzy-rough feature selection (FRFS) approaches are only conducted at the level of individual features, considering the inclusion/exclusion of individual features with regard to a candidate feature subset. In this case, the insight of meaningful information about certain inherent feature structure, such as the correlation between features or the collaborative contribution to a common decision may be ignored. To address this issue, an exclusive lasso assisted two-stage fuzzy-rough FS (EL-TSFRFS) method is presented in this paper. First, regarding discernibility, all features are divided into distinct groups using k-means clustering and the exclusive lasso regularization is utilised to select the representative features in each cluster, with such selected features sorted in descending order within the cluster. Second, a feature grouping-based FRFS algorithm is implemented to further determine the final discriminating feature subset. Comparative experimental results show that the reduct gained by the proposed approach generally outperforms those attained by alternative implementations of FRFS, in terms of both the size of the selected feature subset and the subsequent classification accuracy using the feature subset. Moreover, this is the first work to apply exclusive lasso to fuzzy-rough feature selection.