Feature selection algorithm based on Catastrophe model to improve the performance of regression analysis
Machine Learning
2017-04-25 v1 Machine Learning
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.
Keywords
Cite
@article{arxiv.1704.06656,
title = {Feature selection algorithm based on Catastrophe model to improve the performance of regression analysis},
author = {Mahdi Zarei},
journal= {arXiv preprint arXiv:1704.06656},
year = {2017}
}