Feature selection for regression problems based on the Morisita estimator of intrinsic dimension. (October 2017) - Golay, Jean; Leuenberger, Michael; Kanevski, Mikhail
Highlights: A new supervised filter for regression problems is proposed. The filter uses the newly introduced Morisita estimator of intrinsic dimension. The filter distinguishes between relevant, irrelevant and redundant features. The filter is comprehensively validated using real and simulated datasets. A generic methodology for validating and comparing filters is suggested. Abstract: Data acquisition, storage and management have been improved, while the key factors of many phenomena are not well known. Consequently, irrelevant and redundant features artificially increase the size of datasets, which complicates learning tasks, such as regression. To address this problem, feature selection methods have been proposed. This paper introduces a new supervised filter based on the Morisita estimator of intrinsic dimension. It can identify relevant features and distinguish between redundant and irrelevant information. Besides, it offers a clear graphical representation of the results, and it can be easily implemented in different programming languages. Comprehensive numerical experiments are conducted using simulated datasets characterized by different levels of complexity, sample size and noise. The suggested algorithm is also successfully tested on a selection of real world applications and compared with RReliefF using extreme learning machine. In addition, a new measure of feature relevance is presented and discussed.