A heuristic covering algorithm has higher predictive accuracy than learning all rules

The induction of classification rules has been dominated by a single generic technique—the covering algorithm. This approach employs a simple hill-climbing search to learn sets of rules. Such search is subject to numerous widely known deficiencies. Further, there is a growing body of evidence that learning redundant sets of rules can improve predictive accuracy. The ultimate end-point of a move toward learning redundant rule sets would appear to be to learn and employ all possible rules. This paper presents a learning system that does this. An empirical investigation shows that, while the approach often achieves higher predictive accuracy than a covering algorithm, the covering algorithm outperforms induction of all rules significantly more frequently. Preliminary analysis suggests that learning all rules performs well when the training set clearly defines the decision surfaces but that the heuristic covering algorithm performs better when the decision surfaces are not clearly delineated by the training examples.