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The Scientific World Journal
Volume 2015, Article ID 821798, 9 pages
Research Article

An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset

1Department of Computer Science and Engineering, University College of Engineering, Anna University, Tiruchirappalli, Tamil Nadu, India
2Department of Mathematics, College of Engineering, Anna University, Tamil Nadu, India

Received 2 June 2015; Revised 14 August 2015; Accepted 20 August 2015

Academic Editor: Juan M. Corchado

Copyright © 2015 Senthilkumar Devaraj and S. Paulraj. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Multidimensional medical data classification has recently received increased attention by researchers working on machine learning and data mining. In multidimensional dataset (MDD) each instance is associated with multiple class values. Due to its complex nature, feature selection and classifier built from the MDD are typically more expensive or time-consuming. Therefore, we need a robust feature selection technique for selecting the optimum single subset of the features of the MDD for further analysis or to design a classifier. In this paper, an efficient feature selection algorithm is proposed for the classification of MDD. The proposed multidimensional feature subset selection (MFSS) algorithm yields a unique feature subset for further analysis or to build a classifier and there is a computational advantage on MDD compared with the existing feature selection algorithms. The proposed work is applied to benchmark multidimensional datasets. The number of features was reduced to 3% minimum and 30% maximum by using the proposed MFSS. In conclusion, the study results show that MFSS is an efficient feature selection algorithm without affecting the classification accuracy even for the reduced number of features. Also the proposed MFSS algorithm is suitable for both problem transformation and algorithm adaptation and it has great potentials in those applications generating multidimensional datasets.