Feature Subset Selection Using Genetic Algorithm with Aggressive Mutation for Classification Problem

Marc Jermaine Pontiveros, Geoffrey A. Solano, J. Diaz, Jaime D. L. Caro
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引用次数: 2

Abstract

In this work, the algorithmic approaches to Feature Subset Selection (FSS) are reviewed. FSS is the technique of selecting a subset of relevant features for building parsimonious models. One successful algorithm in selecting relevant features in Brain-Computing Interface is the Genetic Algorithm with Aggressive Mutation (GAAM). We implemented a scikit-learn compatible library for GAAM and determined its applicability with classification tasks in general. Identifying relevant features in a predictive modeling task improves the interpretability of the model, reduces its complexity and the time requirement for training.
基于主动变异遗传算法的特征子集选择
本文对特征子集选择(FSS)的算法方法进行了综述。FSS是一种选择相关特征子集来构建精简模型的技术。一种成功的脑计算接口相关特征选择算法是带有侵略性突变的遗传算法(GAAM)。我们为GAAM实现了一个scikit-learn兼容库,并确定了它在一般分类任务中的适用性。识别预测建模任务中的相关特征可以提高模型的可解释性,降低模型的复杂性和训练时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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