A FRAME WORK TO ELIMINATE IRRELEVANT FEATURES USING HYBRID FEATURE SELECTION ALGORITHM

The recognition/classification of a given pattern is characterized by one of the two following tasks. Supervised classification is a problem of establishing decision regions between patterns and assigning an unknown input pattern into one of the predefined classes. In unsupervised classification, classes are learned based on the similarity of patterns. Supervised 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 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 Hybrid Feature Selection (HFS) method is performed to create well-organized learners. The results of this study shows that the HFS 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.