A supervised solution for redundant feature detection depending on instances

Xue-Qiang Zeng, Guozheng Li
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引用次数: 1

Abstract

As a high dimensional problem, analysis of microarray data sets is a challenging task, where many weakly relevant or redundant features hurt generalization performance of classifiers. The previous works used redundant feature detection methods to select discriminative compact gene set, which only considered the relationship among features, not the redundancy of classification ability among features. Here, we propose a novel algorithm named RESI (Redundant fEature Selection depending on Instance), which considers label information in the measure of feature subset redundancy. Experimental results on benchmark data sets show that RESI performs better than the previous state-of-arts algorithms on redundant feature selection methods like mRMR.
基于实例的冗余特征检测的监督解决方案
作为一个高维问题,微阵列数据集的分析是一项具有挑战性的任务,其中许多弱相关或冗余的特征会影响分类器的泛化性能。以往的研究采用冗余特征检测方法选择判别紧凑基因集,只考虑特征之间的关系,没有考虑特征之间分类能力的冗余性。本文提出了一种基于实例的冗余特征选择(RESI)算法,该算法在特征子集冗余度度量中考虑了标签信息。在基准数据集上的实验结果表明,RESI算法在mRMR等冗余特征选择方法上的表现优于现有算法。
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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