Markov blanket based feature subset selection algorithms

OData support
Dr. Hullám Gábor István
Department of Measurement and Information Systems

Applying feature subset selection (FSS) methods on input data greatly reduces the dimensionality of a problem, thus making it manageable by machine learning methods. It helps already functioning learning systems to be faster and more accurate. It can also be used to discover the relationship between a large number of variables, and to use this knowledge to construct better models.

Approaching FSS by finding a Markov blanket guarantees that the excluded variables are unnecessary given the selected ones. Moreover, if we could find a Markov boundary, that would make the set of selected features minimal.

This Thesis investigates algorithms that can learn a Markov blanket or a Markov boundary from data. We examine them in detail, acknowledge their limits, and propose improvements to them.

We test these algorithms and their modified versions on a set of real data, and a set of artificial data in order to compare them. Based on this we evaluate them and make suggestions for further improvements.


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