To classify high-dimensional data, feature selection plays a key role to eliminate irrelevant attributes and enhance the classification accuracy and efficiency. Since feature selection is an NP-Hard problem, many heuristics and metaheuristics have been used to tackle in practice this problem. In this article, we propose a novel approach that consists in a probabilistic selection hyper-heuristic called the synergy Thompson sampling hyper-heuristic. The Thompson sampling selection strategy is a probabilistic reinforcement learning mechanism to assess the behavior of the low-level heuristics, and to predict which one will be more efficient at each point during the search process. The proposed hyper-heuristic is combined with a 1 nearest neighbor classifier from the Weka framework. It aims to find the best subset of features that maximizes the classification accuracy rate. Experimental results show a good performance in favor of the proposed method when comparing with other existing approaches.

中文翻译：

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用于特征选择问题的协同 Thompson 采样超启发式算法

在对高维数据进行分类时，特征选择对于消除不相关的属性、提高分类精度和效率起着关键作用。由于特征选择是一个 NP-Hard 问题，因此在实践中使用了许多启发式和元启发式来解决这个问题。在本文中，我们提出了一种新颖的方法，它包含一种称为协同汤普森采样超启发式的概率选择超启发式方法。Thompson 抽样选择策略是一种概率强化学习机制，用于评估低级启发式的行为，并预测在搜索过程中的每个点哪个更有效。所提出的超启发式方法与 Weka 框架中的 1 个最近邻分类器相结合。它旨在找到最大化分类准确率的最佳特征子集。实验结果表明，与其他现有方法相比，该方法具有良好的性能。