Inductive Learning in Probabilistic Domain

Yoichiro Nakakuki, Yoshiyuki Koseki, Midori Tanaka

This paper describes an inductive learning method in probabilistic domain. It acquires an appropriate probabilistic model from a small amount of observation data. In order to derive an appropriate probabilistic model, a presumption tree with least description length is constructed. Description length of a presumption tree is defined as the sum of its code length and log-likelihood. Using a constructed presumption tree, the probabilistic distribution of future events can be presumed appropriately from observations of occurrences in the past. This capability enables the efficiency of certain kinds of performance systems, such as diagnostic system, that deal with probabilistic problems. The experimental results show that a model-based diagnostic system performs efficiently by making good use of the learning mechanism. In comparison with a simple probability estimation method, it is shown that the proposed approach requires fewer observations, to acquire an appropriate probabilistic model.

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