An unsupervised feature selection algorithm with feature ranking for maximizing performance of the classifiers

Prediction plays a vital role in decision making. Correct prediction leads to right decision making to save the life, energy, efforts, money and time. The right decision prevents physical and material losses and it is practiced in all the fields including medical, finance, environmental studies, engineering and emerging technologies. Prediction is carried out by a model called classifier. The predictive accuracy of the classifier highly depends on the training datasets utilized for training the classifier. The irrelevant and redundant features of the training dataset reduce the accuracy of the classifier. Hence, the irrelevant and redundant features must be removed from the training dataset through the process known as feature selection. This paper proposes a feature selection algorithm namely unsupervised learning with ranking based feature selection (FSULR). It removes redundant features by clustering and eliminates irrelevant features by statistical measures to select the most significant features from the training dataset. The performance of this proposed algorithm is compared with the other seven feature selection algorithms by well known classifiers namely naive Bayes (NB), instance based (IB1) and tree based J48. Experimental results show that the proposed algorithm yields better prediction accuracy for classifiers. © 2015, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.