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A group incremental feature selection for classification using rough set theory based genetic algorithm

机译：基于粗糙集理论的遗传算法分类的组增量特征选择

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摘要

Data Mining is one of the most challenging tasks in a dynamic environment due to rapid growth of data with respect to time. Dimension reduction, the key process of relevant feature selection, is applied prior to extracting interesting patterns or information from large repositories of data. In a dynamic environment, newly generated group of data together with the information extracted from the previous data are analyzed to select the most relevant and important features of the entire data set. As a result, efficiency and acceptability of the incremental feature selection model increase in the field of data mining. In our paper, a group incremental feature selection algorithm is proposed using rough set theory based genetic algorithm for selecting the optimized and relevant feature subset, called reduct. The objective function of the genetic algorithm used for incremental feature selection is defined using the previously generated reduct and positive region of the target set, concepts of rough set theory. The method may be applied in a regular basis in the dynamic environment after small to moderate volume of data being added into the system and thus the computational time, the major issue of the genetic algorithm does not affect the proposed method. Experimental results on benchmark datasets demonstrate that the proposed method provides satisfactory results in terms of number of selected features, computation time and classification accuracies of various classifiers. (c) 2018 Elsevier B.V. All rights reserved.
机译：由于时间的时间快速增长，数据挖掘是动态环境中最具挑战性的任务之一。在提取来自大型数据存储库的有趣模式或信息之前，应用相关特征选择的关键过程的维度减少。在动态环境中，分析了新生成的数据组以及从先前数据中提取的信息，以选择整个数据集的最相关和最重要的功能。结果，数据挖掘领域增量特征选择模型增加的效率和可接受性。在我们的论文中，使用基于粗糙的集合理论的遗传算法提出了一种组增量特征选择算法，用于选择优化和相关特征子集，称为减析。用于增量特征选择的遗传算法的目标函数使用先前生成的目标集和粗糙集理论的概念来定义了增量特征选择的遗传算法。该方法可以在动态环境中规则地应用于小于中等体积的数据被添加到系统中并因此增加计算时间，因此遗传算法的主要问题不影响所提出的方法。基准数据集的实验结果表明，所提出的方法在各种分类器的所选特征，计算时间和分类准确性的数量方面提供满意的结果。 （c）2018 Elsevier B.v.保留所有权利。

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