{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-multi-objective-anytime-rule-mining-system","title":"A Multi-Objective Anytime Rule Mining System to Ease Iterative Feedback from Domain Experts","arxiv_id":"1812.09746","date":"2018-12-23","proceeding":null,"authors":["Tobias Baum","Steffen Herbold","Kurt Schneider"],"abstract":"Data extracted from software repositories is used intensively in Software\nEngineering research, for example, to predict defects in source code. In our\nresearch in this area, with data from open source projects as well as an\nindustrial partner, we noticed several shortcomings of conventional data mining\napproaches for classification problems: (1) Domain experts' acceptance is of\ncritical importance, and domain experts can provide valuable input, but it is\nhard to use this feedback. (2) The evaluation of the model is not a simple\nmatter of calculating AUC or accuracy. Instead, there are multiple objectives\nof varying importance, but their importance cannot be easily quantified.\nFurthermore, the performance of the model cannot be evaluated on a per-instance\nlevel in our case, because it shares aspects with the set cover problem. To\novercome these problems, we take a holistic approach and develop a rule mining\nsystem that simplifies iterative feedback from domain experts and can easily\nincorporate the domain-specific evaluation needs. A central part of the system\nis a novel multi-objective anytime rule mining algorithm. The algorithm is\nbased on the GRASP-PR meta-heuristic but extends it with ideas from several\nother approaches. We successfully applied the system in the industrial context.\nIn the current article, we focus on the description of the algorithm and the\nconcepts of the system. We provide an implementation of the system for reuse.","url_abs":"http://arxiv.org/abs/1812.09746v1","url_pdf":"http://arxiv.org/pdf/1812.09746v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-multi-objective-anytime-rule-mining-system","repo_url":"https://github.com/tobiasbaum/GIMO-m","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}