A Comparative Analysis of Inductive‐Learning Algorithms

Recently there has been an increasing interest in applying inductive learning algorithms to generate rules/patterns from a given example set. While such approaches serve as an efficient way of resolving the knowledge‐acquisition bottleneck, their predictive accuracy, which is the popular measure of performance, varies widely. This paper contrasts major inductive‐learning algorithms and examines their performance with two performance measures: the predictive accuracy and the representation language. Experiments involved three inductive‐learning algorithms and five different managerial tasks in construction project assessment and bankruptcy‐prediction domains. The test results indicate that the model performance is dependent on tasks with an exception of the neural network model and that there is a an effect of group proportion in the example set used to construct the model. The neural network approach presents relatively stable predictive power across different task domains, although it is difficult to interpret its representation.