Clustering-Based Feature Selection in Semi-supervised Problems
Clustering-Based Feature Selection in Semi-supervised Problems
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DOI：
10.1109/isda.2009.211
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发表时间：
2009-11
期刊：
影响因子：
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通讯作者：
I. Quinzán;J. Sotoca;F. Pla
中科院分区：
文献类型：
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作者：
I. Quinzán;J. Sotoca;F. Pla
在这篇文章中，提出了半监督问题中的特征选择方法。该方法使用基于条件互信息和条件熵的特征聚类策略来选择变量，结合使用监督和无监督特征距离测量。使用训练集中标记和未标记样本之间的不同比例对真实数据库进行了分析，显示了所提出方法的令人满意的行为。
In this contribution a feature selection method in semi-supervised problems is proposed. This method selects variables using a feature clustering strategy, using a combination of supervised and unsupervised feature distance measure, which is based on Conditional Mutual Information and Conditional Entropy. Real databases were analyzed with different ratios between labelled and unlabelled samples in the training set, showing the satisfactory behaviour of the proposed approach.