Rough set theory is one of the eﬀective methods to feature selection, which can preserve the meaning of the features. The essence of rough set approach to feature selection is to ﬁnd a subset of the original features. Since ﬁnding a minimal subset of the features is a NP-hard problem, it is necessary to investigate eﬀective and eﬃcient heuristic algorithms. Ant colony optimization (ACO) has been successfully applied to many diﬃcult combinatorial problems like quadratic assignment, traveling sales- man, scheduling, etc. It is particularly attractive for feature selection since there is no heuristic information that can guide search to the optimal minimal subset every time. However, ants can discover the best feature combinations as they traverse the graph. In this paper, we propose a new rough set approach to feature selection based on ACO, which adopts mutual information based feature signiﬁcance as heuristic information. A novel feature selection algorithm is also given. Jensen and Shen pro- posed a ACO-based feature selection approach which starts from a random feature. Our approach starts from the feature core, which changes the complete graph to a smaller one. To verify the eﬃciency of our algorithm, experiments are carried out on some standard UCI datasets. The results demonstrate that our algorithm can provide eﬃcient solution to ﬁnd a minimal subset of the features.
Mendeley saves you time finding and organizing research
Choose a citation style from the tabs below