GIMO : A multi-objective anytime rule mining system to ease iterative feedback from domain experts

Data extracted from software repositories is used intensively in Software Engineering research, for example, to predict defects in source code. In our research in this area, with data from open source projects as well as an industrial partner, we noticed several shortcomings of conventional data mining approaches for classification problems: (1) Domain experts’ acceptance is of critical importance, and domain experts can provide valuable input, but it is hard to use this feedback. (2) Evaluating the quality of the model is not a matter of calculating AUC or accuracy. Instead, there are multiple objectives of varying importance with hard to quantify trade-offs. Furthermore, the performance of the model cannot be evaluated on a per-instance level in our case, because it shares aspects with the set cover problem. To overcome these problems, we take a holistic approach and develop a rule mining system that simplifies iterative feedback from domain experts and can incorporate the domain-specific evaluation needs. A central part of the system is a novel multi-objective anytime rule mining algorithm. The algorithm is based on the GRASP-PR meta-heuristic but extends it with ideas from several other approaches. We successfully applied the system in the industrial context. In the current article, we focus on the description of the algorithm and the concepts of the system. We make an implementation of the system available. © 2020 The Author

Learning with Globally Predictive Tests 1 Learning with Globally Predictive Tests

Abstract Weintroduce a new bias for rule learning systems. The bias only allows a rule learner to create a rule that predicts class membership if each test of the rule in isolation is predictive of that class. Although the primary motivation for the bias is to improve the understandability of rules, we show that it also improves the accuracy of learned models on anumber of problems. We also introduce a related preference bias that allows creating rules that violate this restriction if they are statistically signi cantly better than alternative rules without such violations

Learning with Globally Predictive Tests

We introduce a new bias for rule learning systems. The bias only allows a rule learner to create a rule that predicts class membership if each test of the rule in isolation is predictive of that class. Although the primary motivation for the bias is to improve the understandability of rules, we show that it also improves the accuracy of learned models on a number of problems. We also introduce a related preference bias that allows creating rules that violate this restriction if they are statistically significantly better than alternative rules without such violations. Keywords Rule Learning; Understandability; Bias x1 Introduction A variety of rule learning systems have been developed that create rules to predict class membership of examples such as AQ15 [1], CN2 [2], ITRULE [3], C4.5-rules[4], FOIL [5], FOCL [6], Greedy3 [7], Ripper [8], and decision lists [9]. One commonly reported advantage of modeling predictive relationships with rules is the comprehensibility of the learned know..