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

## TL;DR

This paper presents a novel feature selection algorithm leveraging the Catastrophe model and Akaike information criterion to enhance regression analysis performance, outperforming existing methods on multiple datasets.

## Contribution

Introduces a new feature selection method based on the Catastrophe model and AIC, providing an alternative to traditional algorithms like RELIEF.

## Key findings

- The proposed algorithm effectively removes irrelevant features.
- It achieves better regression performance than RELIEF.
- Validated on multiple real-world datasets.

## 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.