A well-known fundamental limitation of selective induction algorithms is that when tasksupplied attributes are not adequate for, or directly relevant to, describing hypotheses, their performance in terms of prediction accuracy and/or theory complexity is poor. One solution to this problem is constructive induction. It constructs, by using task-supplied attributes, new attributes that are expected to be more appropriate than the task-supplied attributes for describing the target concepts. This thesis focuses on constructive induction with decision trees as the theory description language. It explores: (1) novel approaches to constructing new binary attributes using existing constructive operators, and (2) novel methods of constructing new nominal and new continuous-valued attributes based on a newly proposed constructive operator. The thesis investigates a fixed rule-based approach to constructing new binary attributes for decision tree learning. It generates conjunctions from production rules that are converted from decision trees using the C4.5rules algorithm. During the process of transforming a decision tree into production rules, C4.5rules eliminates some irrelevant
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