Weighted relative accuracy was proposed in iteilp99-lavrac-flach-zupan as an alternative to classification accuracy typically used in inductive rule learners. Weighted relative accuracy takes into account the improvement of the accuracy relative to the default rule (i.e., the rule stating that the same class should be assigned to all examples), and also explicitly incorporates the generality of a rule (i.e., the number of examples covered). In order to measure the predictive performance of weighted relative accuracy, we implemented it in the rule induction algorithm CN2. Our main results are that weighted relative accuracy dramatically reduces the size of the rule sets induced with CN2 (on average by a factor 9 on the 23 datasets we used), at the expense of only a small average drop in classification accuracy.
|Translated title of the contribution||Predictive Performance of Weighted Relative Accuracy|
|Title of host publication||4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD2000)|
|Publication status||Published - 2000|
Bibliographical noteISBN: 354041066X
Name and Venue of Conference: 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD2000)
Other identifier: 1000516