Feature Selection Using Cooperative Game Theory and Relief Algorithm

With the advancements in various data mining and social network-related approaches, datasets with a very high feature-dimensionality are often used. Various information theoretic approaches have been tried to select the most relevant set of features, and hence bring down the size of the data. Most of the times these approaches try to find a way to rank the features, so as to select or remove a fixed number of features. These principles usually assume some probability distribution for the data. These approaches also fail to capture the individual contribution of every feature in a given set of features. In this paper, we propose an approach which uses the Relief algorithm and cooperative game theory to solve the problems mentioned above. The approach was tested on NIPS 2003 and UCI datasets using different classifiers and the results were comparable to the state-of-the-art methods.