Soft-constrained Laplacian score for semi-supervised multi-label feature selection
Soft-constrained Laplacian score for semi-supervised multi-label feature selection
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DOI：
10.1007/s10115-015-0841-8
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发表时间：
2016-04-01
影响因子：
2.7
通讯作者：
Taleb, Nora
中科院分区：
文献类型：
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作者：
Alalga, Abdelouahid;Benabdeslem, Khalid;Taleb, Nora
特征选择、半监督学习和多标签分类是机器学习和数据挖掘社区面临的不同挑战。虽然其他作品分别解决了这些问题，在本文中，我们将展示如何将它们一起解决。我们提出了一个统一的框架，半监督多标签特征选择，基于拉普拉斯得分。特别是，我们展示了如何约束这个分数的函数，当数据被部分标记，每个实例都与一组标签相关联时。我们将数据的标记部分转换为软约束，并展示如何根据可用的标签将它们集成到特征相关性的度量中。基准数据集上的实验提供了验证所提出的方法，并比较它与其他一些国家的最先进的特征选择方法在多标签的上下文中。
Feature selection, semi-supervised learning and multi-label classification are different challenges for machine learning and data mining communities. While other works have addressed each of these problems separately, in this paper we show how they can be addressed together. We propose a unified framework for semi-supervised multi-label feature selection, based on Laplacian score. In particular, we show how to constrain the function of this score, when data are partially labeled and each instance is associated with a set of labels. We transform the labeled part of data into soft constraints and show how to integrate them in a measure of feature relevance, according to the available labels. Experiments on benchmark data sets are provided for validating the proposed approach and comparing it with some other state-of-the-art feature selection methods in a multi-label context.