Rough Set Based Unsupervised Feature Selection Using Relative dependency Measures

Feature Selection (FS) is a process which attempts to select features which are more informative. It is an important step in knowledge discovery from data. Conventional supervised FS methods evaluate various feature subsets using an evaluation function or metric to select only those features which are related to the decision classes of the data under consideration. However, for many data mining applications, decision class labels are often unknown or incomplete, thus indicating the significance of unsupervised feature selection. However, in unsupervised learning, decision class labels are not provided. The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, we propose a new rough set-based unsupervised feature selection using relative dependency measures. The method employs a backward elimination-type search to remove features from the complete set of original features. As with the classification performance is evaluated using WEKA tool. The method is compared with an existing supervised method and demonstrates that it can effectively remove redundant features.