Feature selection algorithm based on Catastrophe model to improve the performance of regression analysis
This work addresses feature selection for regression tasks, but appears incremental as it builds on existing models without broad impact claims.
The authors tackled the problem of irrelevant or redundant features in regression analysis by introducing a new feature selection algorithm based on the Catastrophe model, which was compared to the RELIEF algorithm and evaluated on datasets like Breast Cancer and Parkinson Telemonitoring, but no concrete performance numbers were provided in the 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.