An Efficient Feature Subset Selection Algorithm for Classification of Multidimensional Dataset

Abstract

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.

Figure 1

The relationship among different classification paradigms.

Figure 2

Proposed MFSS for multidimensional dataset.

Figure 3

Hamming score-Naive Bayes.

Figure 4

Hamming score-SVM.

Figure 5

Hamming score-IBk.

Figure 6

Hamming score-J48.

Figure 7

Exact match: Naive Bayes.

Figure 8

Exact match-SVM.

Figure 9

Exact match-IBk.

Figure 10

Exact match-J48.

Figure 11

Features selected using proposed MFSS.