Abstract
In this paper I propose a novel feature selection technique based on Bayesian networks. The main idea is to exploit the conditional independencies entailed by Bayesian networks in order to discard features that are not directly relevant for classification tasks. An algorithm for learning Bayesian networks and its use in feature selection are illustrated. The advantages of this algorithm with respect to other ones are then discussed. Finally, experimental results are offered which confirm the reliability of the algorithm.
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Freno, A. (2007). Selecting Features by Learning Markov Blankets. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74819-9_9
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DOI: https://doi.org/10.1007/978-3-540-74819-9_9
Publisher Name: Springer, Berlin, Heidelberg
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