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Feature selection algorithm based on Catastrophe model to improve the performance of regression analysis

Abstract · Apr 21, 2017 17:32 ·

selection akaike catastrophe algorithm remove mahdi criterion relief zarei cs-lg stat-ml

Arxiv Abstract

  • Mahdi Zarei

In this paper we introduce a new feature selection algorithm to remove the irrelevant or redundant features in the data sets. In this algorithm the importance of a feature is based on its fitting to the Catastrophe model. Akaike information crite- rion value is used for ranking the features in the data set. The proposed algorithm is compared with well-known RELIEF feature selection algorithm. Breast Cancer, Parkinson Telemonitoring data and Slice locality data sets are used to evaluate the model.

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