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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Semisupervised Feature Selection Based on Relevance and Redundancy Criteria
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Semisupervised Feature Selection Based on Relevance and Redundancy Criteria

机译：基于关联度和冗余度准则的半监督特征选择

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摘要

Feature selection aims to gain relevant features for improved classification performance and remove redundant features for reduced computational cost. How to balance these two factors is a problem especially when the categorical labels are costly to obtain. In this paper, we address this problem using semisupervised learning method and propose a max-relevance and min-redundancy criterion based on Pearson’s correlation (RRPC) coefficient. This new method uses the incremental search technique to select optimal feature subsets. The new selected features have strong relevance to the labels in supervised manner, and avoid redundancy to the selected feature subsets under unsupervised constraints. Comparative studies are performed on binary data and multicategory data from benchmark data sets. The results show that the RRPC can achieve a good balance between relevance and redundancy in semisupervised feature selection. We also compare the RRPC with classic supervised feature selection criteria (such as mRMR and Fisher score), unsupervised feature selection criteria (such as Laplacian score), and semisupervised feature selection criteria (such as sSelect and locality sensitive). Experimental results demonstrate the effectiveness of our method.
机译：特征选择旨在获得相关特征以改善分类性能，并删除冗余特征以降低计算成本。如何平衡这两个因素是一个问题，尤其是当分类标签的获取成本很高时。在本文中，我们使用半监督学习方法解决了这一问题，并基于Pearson相关系数（RRPC）提出了最大关联和最小冗余准则。这种新方法使用增量搜索技术来选择最佳特征子集。新选择的要素以有监督的方式与标签具有强烈的相关性，并避免了在无监督约束下对所选要素子集的冗余。对基准数据集中的二进制数据和多类别数据进行了比较研究。结果表明，在半监督特征选择中，RRPC可以在相关性和冗余之间实现良好的平衡。我们还将RRPC与经典的监督特征选择标准（例如mRMR和Fisher评分），无监督特征选择标准（例如Laplacian评分）和半监督特征选择标准（例如sSelect和位置敏感）进行比较。实验结果证明了该方法的有效性。

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