A predictive model using improved Normalized Point Wise Mutual Information (INPMI)

In machine learning, selection of optimal features for the classifier is a critical problem. In order to address this problem a novel feature selection method named “Improved Normalized Point wise Mutual Information (INPMI)” is proposed. The proposed INPMI method coupled with Sequential forward search (SFS) finds the best feature subset to aid feature selection process. The key properties of evaluating feature subset i.e. relevancy and redundancy are analysed well. The classifiers like Naive Bayes, Support Vector Machine and J48 are used to determine the accuracy for the choice of features selected. Experimental results with benchmark medical datasets from UCI (University of California Irvine) machine learning database show that proposed INPMI-NB model with SFS, INPMI-SVM model with SFS, INPMI-J48model with SFS achieves 98.36 %, 98.90 %, 94.53 % classification accuracy and selects 22 features for erythemato-squamous diseases. Also the proposed work is evaluated on a World Aircraft dataset to prove its generalization ability. Experimental results prove that the proposed INPMI method outperforms the existing systems.