Markov blanket feature selection with non-faithful data distributions
Date
2013
Authors
Yu, K.
Wu, X.
Zhang, Z.
Mu, Y.
Wang, H.
Ding, W.
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Conference paper
Citation
Proceedings - IEEE International Conference on Data Mining, 2013, iss.6729570, pp.857-866
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13th IEEE International Conference on Data Mining (7 Dec 2013 - 10 Dec 2013 : Dallas, USA)
Abstract
In faithful Bayesian networks, the Markov blanket of the class attribute is a unique and minimal feature subset for optimal feature selection. However, little attention has been paid to Markov blanket feature selection in a non-faithful environment which widely exists in the real world. To tackle this issue, in this paper, we deal with non-faithful data distributions and propose the concept of representative sets instead of Markov blankets. With a standard sparse group lasso for selection of features from the representative sets, we design an effective algorithm, SRS, for Markov blanket feature Selection via Representative Sets with non-faithful data distributions. Empirical studies demonstrate that SRS outperforms the state-of-the-art Markov blanket feature selectors and other well-established feature selection methods.
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Copyright 2013 IEEE