Selecting Efficient Features via a Hyper-Heuristic Approach

By Emerging huge databases and the need to efficient learning algorithms on these datasets, new problems have appeared and some methods have been proposed to solve these problems by selecting efficient features. Feature selection is a problem of finding efficient features among all features in which the final feature set can improve accuracy and reduce complexity. One way to solve this problem is to evaluate all possible feature subsets. However, evaluating all possible feature subsets is an exhaustive search and thus it has high computational complexity. Until now many heuristic algorithms have been studied for solving this problem. Hyper-heuristic is a new heuristic approach which can search the solution space effectively by applying local searches appropriately. Each local search is a neighborhood searching algorithm. Since each region of the solution space can have its own characteristics, it should be chosen an appropriate local search and apply it to current solution. This task is tackled to a supervisor. The supervisor chooses a local search based on the functional history of local searches. By doing this task, it can trade of between exploitation and exploration. Since the existing heuristic cannot trade of between exploration and exploitation appropriately, the solution space has not been searched appropriately in these methods and thus they have low convergence rate. 1 ،نامرک ،رنهاب دیهش هاگشناد ،رتویپماک یسدنهم هورگ ،یعونصم شوه دشرا یسانشراک يوجشناد [email protected] * نامرک رنهاب دیهش هاگشناد ،ناوج نارگشهوژپ نمجنا وضع . 2 ،رایداتسا ،نامرک ،رنهاب دیهش هاگشناد ،رتویپماک یسدنهم هورگ [email protected] 3 ،رایداتسا ،نامرک ،رنهاب دیهش هاگشناد ،رتویپماک یسدنهم هورگ [email protected] For the first time, in this paper use a hyper-heuristic approach to find an efficient feature subset. In the proposed method, genetic algorithm is used as a supervisor and 16 heuristic algorithms are used as local searches. Empirical study of the proposed method on several commonly used data sets from UCI data sets indicates that it outperforms recent existing methods in the literature for feature selection.