Game Theory (GT) is the study of strategic decision making. By virtue of its importance, several GT based methodologies for Feature Selection (FS) are proposed in recent times. FS problem can be abstracted as a game by considering each feature as a player and their values as their strategies. Additionally, overall goal of the game is set to classify a data instance appropriately. Most of the existing GT based FS techniques are restricted to Zero Sum Games, Non-Zero Sum Games and Cooperative Games. The classical setting of assuming that all the details of players are known to all players cannot hold in many real-world problems. When the given features are independent, they cannot be treated alike and a characteristics based uncertainty persists among the features. This uncertainty is handled by none of the game forms used in the existing methods. Unlike the mentioned game techniques, Bayesian Games (BG) address the games with imperfect information. This paper investigate the FS problem in terms of BG and proposes a novel method to select the best features. The proposed BG based FS method is a filter type FS method and it starts with identifying Principle Features (PF) and proceeds to play global pairwise Bayesian games between those PF to obtain feature scores. Later, the features are ranked using these scores. In the final stage, a forward selection method with Support Vector Machine (SVM) is used to evaluate the classification performance of the ranked features and helps in the selection of the optimal set of features. Besides these, to improve the scalability of the proposed method MapReduce paradigm is exploited. In order to show the efficacy of the proposed method, experiments are carried out with seven real-world datasets from UCI and Statlog repositories. The promising results showed a significant improvement in the classification performance with fewer selected features than which is achieved using the existing methods.

博弈论 (GT) 是对战略决策制定的研究。由于其重要性，最近提出了几种基于 GT 的特征选择 (FS) 方法。FS 问题可以抽象为一个游戏，通过将每个特征视为玩家并将它们的值作为策略。此外，游戏的总体目标被设置为适当地对数据实例进行分类。大多数现有的基于 GT 的 FS 技术仅限于零和博弈、非零和博弈和合作博弈。假设所有玩家都知道玩家的所有详细信息的经典设置无法解决许多现实世界的问题。当给定的特征是独立的时，它们不能被相似对待，并且基于特征的不确定性在特征之间持续存在。现有方法中使用的游戏形式都无法处理这种不确定性。与上述博弈技术不同，贝叶斯博弈 (BG) 解决的是不完全信息博弈。本文根据 BG 研究 FS 问题，并提出了一种选择最佳特征的新方法。所提出的基于 BG 的 FS 方法是一种过滤器类型的 FS 方法，它从识别主要特征 (PF) 开始，然后在这些 PF 之间进行全局成对贝叶斯博弈以获得特征分数。之后，使用这些分数对特征进行排名。在最后阶段，使用支持向量机（SVM）的前向选择方法来评估排序特征的分类性能，并帮助选择最佳特征集。除此之外，为了提高所提出方法的可扩展性，还利用了 MapReduce 范例。为了显示所提出方法的有效性，使用来自 UCI 和 Statlog 存储库的七个真实世界数据集进行了实验。有希望的结果表明，与使用现有方法相比，选择的特征更少，分类性能有了显着提高。