Inducing Cost-Sensitive Non-Linear Decision Trees
نویسنده
چکیده
This paper presents a new decision tree learning algorithm that takes account of costs of misclassification. The algorithm is based on the hypothesis that non-linear decision nodes provide a better basis for cost-sensitive induction than axis-parallel decision nodes and utilizes discriminant analysis to construct non-linear cost-sensitive decision trees. The performance of the algorithm is evaluated by applying it to seven data sets and the results compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost. MetaCost is applied both with a base learner that uses axis-parallel spits and a base learner that uses non-linear splits, thereby enabling an evaluation of the value of non-linear decision nodes. The results show that the new algorithm displays a better profile than ICET as the ratio of costs of misclassification departs from unity and that it provides a better base learner for MetaCost than an axis-parallel learner.
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