Simultaneous Feature Selection and Tuple Selection for Efficient Classification

Abstract

Feature selection and tuple selection help the classifier to focus to achieve similar (or even better) accuracy as compared to the classification without feature selection and tuple selection. Although feature selection and tuple selection have been studied earlier in various research areas such as machine learning, data mining, and so on, they have rarely been studied together. The contribution of this chapter is that the authors propose a novel distance measure to select the most representative features and tuples. Their experiments are conducted over some microarray gene expression datasets, UCI machine learning and KDD datasets. Results show that the proposed method outperforms the existing methods quite significantly.