Introducing the Theory of Probabilistic Hierarchical Learning for Classification

This is the 5th paper in our series of papers on hierarchical learning for classification. Hierarchical learning for classification is an automated method of creating hierarchy list of learnt models that are on the one hand capable of partitioning the training set into equal number of subsets and on the other hand are also capable of classifying elements of each corresponding subset into classes of the problem. In this paper, the probabilistic hierarchical learning for classification has been formalized and presented as a theory. The theory asserts that the accurate models of complex datasets can be produced through hierarchical application of low complexity models. The theory is validated through experiments on five popular real-world datasets. Generalizing ability of the theory is also tested. Comparison with the contemporary literature points towards promising future for this theory. The theory is covered by four postulates, which are carved out elegantly through mathematical formalisms.