通过超启发式方法选择有效特征
Selecting Efficient Features via a Hyper-Heuristic Approach
摘要：
随着大型数据库的出现以及对这些数据集的高效学习算法的需求，出现了新的问题，并提出了一些通过选择高效特征来解决这些问题的方法。特征选择是在所有特征中寻找有效特征的问题，在这些特征中，最终的特征集可以提高准确性并降低复杂度。解决这个问题的一种方法是评估所有可能的特征子集。然而，评估所有可能的特征子集是穷举搜索，因此它具有高计算复杂性。到目前为止，许多启发式算法已被研究解决这个问题。超启发式算法是一种新的启发式算法，它通过适当的局部搜索，可以有效地搜索解空间。每个局部搜索是一个邻域搜索算法。由于解空间中的每个区域都有其自身的特点，因此应选择适当的局部搜索并将其应用于当前解。这项任务交给了主管。主管根据本地搜索的功能历史选择本地搜索。通过这样做，它可以在开发和勘探之间进行交易。由于现有的启发式算法不能在探索和利用之间进行恰当的权衡，这些方法没有对解空间进行适当的搜索，因此收敛速度较慢。本文首次采用超启发式方法寻找有效的特征子集。该方法以遗传算法为监督算法，以16种启发式算法为局部搜索算法。在UCI数据集上的几个常用数据集上的实证研究表明，该方法优于文献中现有的特征选择方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
