事象ごとの生起確率から未知事象発見を支援する手法とそのアンケート調査への適用

概要

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The real world includes various unknown elements, objects, phenomena, and so on. Causes of phenomena to be observed is difficult to specify, because they might be uncoverable for the observer. That is, it takes too much time to consider all the possible causes, or sometimes they are even unable to be considered because they are unknown. Now then, we aim at constructing a learning method by which to learn unknown causal-links and unknown basic causes, i.e., deeper causes than those assumable as hypotheses. A major contribution of this paper is that the input data are not combinations of simultaneous occurence of events, but the average occurence of each event given as probabilities. By determining the position of new nodes in the probabilitic network, the user reasons for him/herself, what the new node means in the real world. The system is applied to analyzing data obtained by a few questionaires.
1999-03-01