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arXiv · 1704.06656

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

Mahdi Zarei
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Abstract

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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Mahdi Zarei. 2017-04-21. Feature selection algorithm based on Catastrophe model to improve the performance of regression analysis. https://arxiv.org/abs/1704.06656

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