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首页> 外文会议>2008高等智能国际会议（2008 International Conference on Advanced Intelligence）论文集 >Redundant Feature Elimination, a Supervised Solution
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Redundant Feature Elimination, a Supervised Solution

机译：冗余功能消除，受监督的解决方案

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摘要

It is a challenging problem to analyze high dimensional data sets like microarray data, where many irrelevant and weakly relevant but redundant features hurt generalization performance of classifiers. Irrelevant features are considered in most of previous works, are removed by considering label information. Weakly relevant but redundant features are only considered by a few works, which considered using linear or nonlinear filters. But these filters do not consider utilizing the label information to obtain discriminative contribution of each feature and furthermore eliminate those features with little discriminative contribution. Here we propose a novel supervised metric based on discriminative contribution to perform redundant feature elimination. By the new metric, complementary features are likely to be preserved, which is beneficial for the final classification. Experimental results on three microarray data sets show our proposed metric for redundant feature elimination based on discriminative contribution is better than the previous state-of-arts linear or nonlinear metrics on the problem of analysis of microarray data sets.
机译：分析诸如微阵列数据之类的高维数据集是一个具有挑战性的问题，其中许多不相关和弱相关但冗余的特征会损害分类器的泛化性能。在以前的大多数作品中都考虑了不相关的功能，通过考虑标签信息将其删除。仅有少数工作考虑了弱相关但多余的特征，这些工作考虑使用线性或非线性滤波器。但是这些过滤器没有考虑利用标签信息来获得每个特征的歧视性贡献，并且进一步消除了具有很少歧视性贡献的那些特征。在这里，我们提出了一种基于歧视性贡献的新型监督指标，以执行冗余特征消除。通过新的度量标准，可能会保留互补特征，这对于最终分类是有利的。在三个微阵列数据集上的实验结果表明，我们提出的基于判别贡献的冗余特征消除度量优于关于微阵列数据集分析问题的现有技术的线性或非线性度量。

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