A Filter Based Feature Set Selection Approach for Big Data Classification of Patient Records

The flourishing fame and development of big data in recent years made researchers to have a detailed study. Of the all entire emerging big data research topics, classification of data from big data is identified as a great challenge to address as of our analysis. The Classification is the process of categorizing data for its most effective and efficient use. While analyzing large scale patient records, hierarchical learning approach which is tree structured that train max-margin classifier will give better classification results and also it is computationally efficient. The quality of features has an effect on the performance of hierarchical learning approach for classification of patient records. So we have to extract discriminative features for training hierarchical classifier. In this paper Highly Correlated Feature Set Selection (HCFS) algorithm is proposed to combine with the hierarchical leaning approach to improve its performance. This algorithm identifies the good feature subsets which will improve the classification accuracy.