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A Differential Evolution Approach to Feature Selection and Instance Selection

机译：特征选择和实例选择的差分进化方法

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More and more data is being collected due to constant improvements in storage hardware and data collection techniques. The incoming flow of data is so much that data mining techniques cannot keep up with. The data collected often has redundant or irrelevant features/instances that limit classification performance. Feature selection and instance selection are processes that help reduce this problem by eliminating useless data. This paper develops a set of algorithms using Differential Evolution to achieve feature selection, instance selection, and combined feature and instance selection. The reduction of the data, the classification accuracy and the training time are compared with the original data and existing algorithms. Experiments on ten datasets of varying difficulty show that the newly developed algorithms can successfully reduce the size of the data, and maintain or increase the classification performance in most cases. In addition, the computational time is also substantially reduced. This work is the first time for systematically investigating a series of algorithms on feature and/or instance selection in classification and the findings show that instance selection is a much harder task to solve than feature selection, but with effective methods, it can significantly reduce the size of the data and provide great benefit.
机译：由于存储硬件和数据收集技术的不断改进，正在收集越来越多的数据。传入的数据流是如此之大，以至于数据挖掘技术无法跟上。收集的数据通常具有冗余或不相关的特征/实例，从而限制了分类性能。功能选择和实例选择是通过消除无用的数据来帮助减少此问题的过程。本文开发了一套使用差分进化的算法，以实现特征选择，实例选择以及组合的特征和实例选择。将数据的约简，分类精度和训练时间与原始数据和现有算法进行比较。在十个难度不同的数据集上进行的实验表明，新开发的算法可以在大多数情况下成功减小数据量，并保持或提高分类性能。另外，计算时间也大大减少了。这项工作是第一次系统地研究关于分类中特征和/或实例选择的一系列算法，研究结果表明，实例选择比特征选择更难解决，但是有了有效的方法，它可以大大减少特征选择。数据的大小并提供巨大的好处。

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