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A REVIEW OF FEATURE SELECTION TECHNIQUES IN STRUCTURE LEARNING

Muhammad Naeem*, Sohail Asghar**

* Department of Computer Science, Mohammad Ali Jinnah University Islamabad Pakistan
** University Institute of Information Technology, PMAS-Arid Agriculture University, Rawalpindi Pakistan

ABSTRACT

In the last two decades, there has been significant advancement in heuristics for inducing Bayesian belief networks for the purpose of automatic distillation of knowledge from masses of data with target concepts. However, there are various circumstances where we are confronted to fix a set of most influencing variables in modelling of class variable. This arises in provision of confidence measures on set of variables used in the structure learning of data. In this study, we have tweaked empirical as well as theoretical aspects of various feature selection evaluators, their corresponding searching methods under six well known scoring functions in K2 which is a notable structure learning technique in Bayesian belief network. We have come up with some useful findings for overall computationally efficient approach among eleven evaluators. This analysis is useful in inducing better structure from the given dataset in imparting improved performance metric for Bayesian belief network.
 

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Journal of Engineering and Applied Sciences

December

Vol. 40, Iss. 2, pp. 91-134

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