Tarun Maini

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Dimensionality reduction and identification of relevant features are important for the classification accuracy. Selecting large number of features increases computational complexity whereas selection of too few features may not contain sufficient information required for the classification. This paper presents the comparative performance of different(More)
A method of feature selection using elitist Genetic Algorithm is proposed in this work. Stratified-tenfold-cross-validation classification accuracy is used as fitness function. The method developed can detect redundant and irrelevant features, consequently producing the optimal feature set. The algorithm is carried out on the four benchmark datasets.(More)
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