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ICDM
2005
IEEE

Speculative Markov Blanket Discovery for Optimal Feature Selection

9 years 5 months ago
Speculative Markov Blanket Discovery for Optimal Feature Selection
In this paper we address the problem of learning the Markov blanket of a quantity from data in an efficient manner. Markov blanket discovery can be used in the feature selection problem to find an optimal set of features for classification tasks, and is a frequently-used preprocessing phase in data mining, especially for high-dimensional domains. Our contribution is a novel algorithm for the induction of Markov blankets from data, called Fast-IAMB, that employs a heuristic to quickly recover the Markov blanket. Empirical results show that Fast-IAMB performs in many cases faster and more reliably than existing algorithms without adversely affecting the accuracy of the recovered Markov blankets.
Sandeep Yaramakala, Dimitris Margaritis
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where ICDM
Authors Sandeep Yaramakala, Dimitris Margaritis
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