Rule Quality Measures Settings in Classification, Regression and Survival Rule Induction — an Empirical Approach

GuideR: A guided separate-and-conquer rule learning in classification, regression, and survival settings

“…The efficiency of our algorithms for automatic rule induction has been confirmed on dozens of benchmark datasets [19,20,21,22]. In the experimental part of this article we focused on showing the efficiency and benefits coming from the use of the guided version of the algorithm.…”

“…In the works [19,20,21,22], we have presented and confirmed on dozens of benchmark datasets the effectiveness of our version of the sequential algorithm for generating classification, regression, and survival rules. This article presents the semi-interactive version of that algorithm, which overcomes the largest limitation of the existing rule induction methods-the lack of the possibility to introduce user's knowledge (or expert's knowledge) to the learning process.…”

“…Here we describe the fully automatic procedure-the userguided variant is presented in the next subsection. An important factor determining performance and comprehensibility of the resulting model is a selection of a rule quality measure [61,62,63,20] (rule learning heuristic [64,65,66]) that supervises the rule induction process. In the case of classification problems, our software provides user with a number of state-of-art measures calculated on the basis of the rule confusion matrix.…”

“…Particularly, when class L k is investigated, the set D is binarized with respect to it: examples with labels equal to L k are positives, while the other are negatives. The detailed information about our algorithm for classification, regression, and survival rule induction using separate-and-conquer strategy can be found in [20,19]. The most important limitation of the presented approach is that induction is fully automatic-the user may control how the model looks like only by selecting quality measure and adjusting mincov parameter.…”

“…After that, if Σ auto flag is enabled, the algorithm adds automatically induced conditions using standard separate-and-conquer strategy (lines 6-9). When all initial rules have been processed, new ones are generated analogously; the corresponding boolean parameters are called Υ pref and Υ auto (lines [11][12][13][14][15][16][17][18][19][20].…”