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A Bayesian network is a directed acyclic graph in which each node represents a variable and each arc a probabilistic dependency; they are used to provide: a compact form to represent the knowledge and flexible methods of reasoning. Obtaining it from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper we define an automatic learning method that optimizes the Bayesian networks applied to classification, using a hybrid method of learning that combines the advantages of the induction techniques of the decision trees (TDIDT-C4.5) with those of the Bayesian networks. The resulting method is applied to prediction in health domain.

In the context of learning classification knowledge by inquiry examples, how to help students perform induction learning more efficiently and meaningfully with adaptive instruction strategies is an important research issue. This paper presents a machine-learning approach to developing computer-assisted learning supports for inductive learning tasks by providing individualized inquiry examples. A learning process based on the knowledge refinement strategy is first proposed to model the learning task of induction by inquiry examples. Several machine-learning techniques are developed and evaluated to support various instruction/learning activities such as evaluating student learning outcomes, providing appropriate examples for students to study, and giving individualized hints to motivate and facilitate learning during the induction learning process. Integrated with the intelligent learning supports, a web-based system, named ALBIX (Active Learning By Inquiry Examples), was implemented so that students can actively construct, verify and refine their classification knowledge in an interactive manner. Finally, the learning supports presented in this paper are shown to be effective in their design purposes through a set of simulation tests. A small-scaled prototype testing also showed that teachers and students might be interested in such kind of active learning strategy.