An improved hybrid feature selection method for huge dimensional datasets

Abstract

High dimensions of data cause overfitting in machine learning models, can lead to reduction in accuracy during classification of instances. Variable selection is the most essential function in predictive analytics, that reduces the dimensionality, without losing an appropriate information by selecting a few significant features of machine learning problems. The major techniques involved in this process are filter and wrapper methodologies. While filters measure the weight of features based on the attribute weighting criterion, the wrapper approach computes the competence of the variable selection algorithms. The wrapper approach is achieved by the selection of feature subgroups by pruning the feature space in its search space. The objective of this paper is to choose the most favourable attribute subset from the novel set of features, by using the combination method that unites the merits of filters and wrappers. To achieve this objective, an Improved Hybrid Feature Selection (IHFS) method is performed to create well-organized learners. The results of this study shows that the IHFS algorithm can build competent business applications, which have got a better precision than that of the constructed which is stated by the previous hybrid variable selection algorithms. Experimentation with UCI (University of California, Irvine) repository datasets affirms that this method have got better prediction performance, more robust to input noise and outliers, balances well with the available features, when performed comparison with the present algorithms in the literature review.