Fast Markov blanket discovery

In this thesis, we address the problem of learning the Markov blanket of a quantity from data in an efficient manner. The discovery of the Markov blanket is useful in the feature subset selection problem and in discovering the structure of a Bayesian network. Our contribution is a novel algorithm called FAST-IAMB for the induction of Markov blankets that employs a fast heuristic to quickly converge to the Markov blanket. Empirical results show that the algorithm performs reasonably faster than existing algorithms. In addition, the results also show that the speedup does not adversely affect the accuracy of the recovered Markov blankets

Fast Markov blanket discovery

In this thesis, we address the problem of learning the Markov blanket of a quantity from data in an efficient manner. The discovery of the Markov blanket is useful in the feature subset selection problem and in discovering the structure of a Bayesian network. Our contribution is a novel algorithm called FAST-IAMB for the induction of Markov blankets that employs a fast heuristic to quickly converge to the Markov blanket. Empirical results show that the algorithm performs reasonably faster than existing algorithms. In addition, the results also show that the speedup does not adversely affect the accuracy of the recovered Markov blankets.</p

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. 1

Mining Bayesian Networks from Streamed Data

Bayesian networks, created by Judea Pearl [Pea98], have became important as a model of uncertainty, giving us an easily understandable way to see relationships between attributes of a set o

DEDICATION

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