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Semi-Supervised Learning Based on Information Theory and Functional Dependency Rules of Probability

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The problems of unlabeled data and missing values are two hot research topics in machine learning and pattern recognition. In this paper, we proposed a novel algorithm called FFDC, which chooses Naive Bayes as the underlying supervised learner in the semi-supervised learning framework. Based on the conditional independence assumption of Naive Bayes, the information gain of Information theory is redefined to quantitatively measure the information implicated in unlabeled data. And functional dependency rules of probability are deduced based on Armstrong's axioms, which can be used to find and delete redundant attributes. Thus the computational complexity while modeling will be reduced exponentially. Empirical studies on a set of natural domains show that FFDC has clear advantages with respect to generalization and probabilistic performance.
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Keywords: MACHINE LEARNING; MISSING VALUES; NAIVE BAYES; SEMI-SUPERVISED LEARNING; UNLABELED DATA

Document Type: Research Article

Publication date: February 1, 2011

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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