Abstract
Most decision tree induction methods used for extracting knowledge in classification problems do not deal with cognitive uncertainties such as vagueness and ambiguity associated with human thinking and perception. In this paper cognitive uncertainties involved in classification problems are explicitly represented, measured, and incorporated into the knowledge induction process. A fuzzy decision tree induction method, which is based on the reduction of classification ambiguity with fuzzy evidence, is developed. Fuzzy decision trees represent classification knowledge more naturally to the way of human thinking and are more robust in tolerating imprecise, conflict, and missing information.
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