A novel mutual dependence measure in structure learning

Mutual dependence between features plays an important role in the formulation of classifiers, clustering and other machine intelligent techniques. In this study a novel measure of mutual information known as integration to segregation (I2S), explaining the relationship between the two features is proposed. Some important characteristics of the proposed measure was investigated and its performance in terms of class imbalance measures was compared. It was shown that I2S possesses the characteristics, which are useful in controlling overfitting problems. In structure learning techniques such as Bayesian belief networks, conventional measures of dependency relationship cope with the overfitting problem by restricting the number of parents for a node; however it is still not impressive because complete overfitting is not eliminated. In contrast, I2S is capable of significantly maximizing the discriminant function with a better control of overfitting in the formulation of structure learning.

J.Natn.Sci.Foundation Sri Lanka 2013 41 (3): 203-208

DOI: http://dx.doi.org/10.4038/jnsfsr.v41i3.6054