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MCES: A Novel Monte Carlo Evaluative Selection Approach for Objective Feature Selections

机译：MCES：一种用于目标特征选择的新型蒙特卡洛评估选择方法

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摘要

Most recent research efforts on feature selection have focused mainly on classification task due to its popularity in the data-mining community. However, feature selection research in nonlinear system estimations has been very limited. Hence, it is reasonable to devise a feature selection approach that is computationally efficient on nonlinear system estimations context. A novel feature selection approach, the Monte Carlo evaluative selection (MCES), is proposed in this paper. MCES is an objective sampling method that derives a better estimation of the relevancy measure. The algorithm is objectively designed to be applicable to both classification and nonlinear regressive tasks. The MCES method has been demonstrated to perform well with four sets of experiments, consisting of two classification and two regressive tasks. The results demonstrate that the MCES method has following strong advantages: 1) ability to identify correlated and irrelevant features based on weight ranking, 2) application to both nonlinear system estimation and classification tasks, and 3) independence of the underlying induction algorithms used to derive the performance measures
机译：由于特征选择在数据挖掘社区中的流行，最近有关特征选择的研究主要集中在分类任务上。但是，非线性系统估计中的特征选择研究非常有限。因此，设计一种在非线性系统估计上下文中计算效率高的特征选择方法是合理的。本文提出了一种新颖的特征选择方法，即蒙特卡洛评估选择（MCES）。 MCES是一种客观的抽样方法，可以更好地估计相关性。该算法经过客观设计，适用于分类和非线性回归任务。事实证明，MCES方法在四组实验（包括两个分类和两个回归任务）中表现良好。结果表明，MCES方法具有以下强大优势：1）能够基于权重排序识别相关特征和不相关特征； 2）在非线性系统估计和分类任务中的应用； 3）用于导出的底层归纳算法的独立性绩效指标

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