Abstract
The selection of optimal subset of features from high-dimensional data sets still remains a major challenge during breast cancer detection and categorization. There exist several research works regarding optimal feature subset selection from high-dimensional data sets, but the obtained results are not satisfying when multidimensional data sets (MDDs) are employed in large amount during disease analysis. In this article, an effective feature subset selection and classification method suitable for MDD is proposed. At first, the important and distinct features are extracted from the mammogram images using a Deep Neural Network with wrapper-based extraction technique. Then, a novel two-phase mutation strategy integrated with grey wolf optimizer algorithm is employed for selecting the most relevant feature subsets. Finally, a learning-based semilazy Bayesian network classifier with parallel implementation is proposed for the precise categorization of the breast cancer stages. The proposed method is executed in MATLAB platform and analyzed using mammogram images taken from MAMMOSET database. The proposed method is likened with three state-of-the-art existing feature subset selection and classification approaches for validating the efficiency of the proposed approach. For Data set 1, the proposed method shows an accuracy of 90%, 92% and 98%, which is better than the existing methods taken for comparison. Also for Data set 2, the proposed method shows an accuracy of 97%, 91% and 94%, which is much better than the accuracy achieved by the existing approaches. Thus, the proposed approach outperforms the compared existing approaches by providing better precision, recall, F-measure, specificity and accuracy.