  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. 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.
