Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Machine Learning
Time and sample efficient discovery of Markov blankets and direct causal relations
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
LS Bound based gene selection for DNA microarray data
Bioinformatics
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Structured machine learning: the next ten years
Machine Learning
Feature selection with dynamic mutual information
Pattern Recognition
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Feature selection is an important preprocessing step for building efficient, generalizable and interpretable classifiers on high dimensional data sets. Given the assumption on the sufficient labelled samples, the Markov Blanket provides a complete and sound solution to the selection of optimal features, by exploring the conditional independence relationships among the features. In real-world applications, unfortunately, it is usually easy to get unlabelled samples, but expensive to obtain the corresponding accurate labels on the samples. This leads to the potential waste of valuable classification information buried in unlabelled samples. In this paper, we propose a new BAyesian Semi-SUpervised Method, or BASSUM in short, to exploit the values of unlabelled samples on classification feature selection problem. Generally speaking, the inclusion of unlabelled samples helps the feature selection algorithm on (1) pinpointing more specific conditional independence tests involving fewer variable features and (2) improving the robustness of individual conditional independence tests with additional statistical information. Our experimental results show that BASSUM enhances the efficiency of traditional feature selection methods and overcomes the difficulties on redundant features in existing semi-supervised solutions.