2019/08/23 by Thiago Zafalon Miranda, Miranda, Thiago Zafalon, Diorge Brognara Sardinha +3 · 2 citations
Computer Science · #68T05 #68T20 #68T30 #Artificial Intelligence (cs.AI) #Artificial intelligence #Classifier (UML) #Computer science #Data Stream Mining Techniques #Data mining #Evolutionary Algorithms and Applications #FOS: Computer and information sciences #Feature vector #Imbalanced Data Classification Techniques #Interpretability #Machine Learning (cs.LG) #Machine learning #Novelty #Perspective (graphical) #cs.AI #cs.LG #msc:68T05 #msc:68T20 #msc:68T30
paper · pdf · doi:10.48550/arxiv.1908.09652
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2019/08/23 · arxiv created 2020/03/30 · arxiv updated 2020/03/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years, the interest in interpretable classification models has grown. One of the proposed ways to improve the interpretability of a rule-based classification model is to use sets (unordered collections) of rules, instead of lists (ordered collections) of rules. One of the problems associated with sets is that multiple rules may cover a single instance, but predict different classes for it, thus requiring a conflict resolution strategy. In this work, we propose two algorithms capable of finding feature-space regions inside which any created rule would be consistent with the already existing rules, preventing inconsistencies from arising. Our algorithms do not generate classification models, but are instead meant to enhance algorithms that do so, such as Learning Classifier Systems. Both algorithms are described and analyzed exclusively from a theoretical perspective, since we have not modified a model-generating algorithm to incorporate our proposed solutions yet. This work presents the novelty of using conflict avoidance strategies instead of conflict resolution strategies.