Building an interpretable fuzzy rule base from data using Orthogonal Least Squares Application to a depollution problem

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dc.creatorDestercke, Sébastien
dc.creatorGuillaume, Serge
dc.creatorCharnomordic, Brigitte
dc.date2008-08-21
dc.date.accessioned2026-07-07T09:57:43Z
dc.date.available2026-07-07T09:57:43Z
dc.descriptionIn many fields where human understanding plays a crucial role, such as bioprocesses, the capacity of extracting knowledge from data is of critical importance. Within this framework, fuzzy learning methods, if properly used, can greatly help human experts. Amongst these methods, the aim of orthogonal transformations, which have been proven to be mathematically robust, is to build rules from a set of training data and to select the most important ones by linear regression or rank revealing techniques. The OLS algorithm is a good representative of those methods. However, it was originally designed so that it only cared about numerical performance. Thus, we propose some modifications of the original method to take interpretability into account. After recalling the original algorithm, this paper presents the changes made to the original method, then discusses some results obtained from benchmark problems. Finally, the algorithm is applied to a real-world fault detection depollution problem.
dc.descriptionpre-print of final version published in Fuzzy Sets and Systems
dc.identifierhttps://arxiv.org/abs/0808.2984
dc.identifierhttp://arxiv.org/abs/0808.2984
dc.identifierFuzzy Sets and Systems 158, 18 (2007) 2078-2094
dc.identifierdoi:10.1016/j.fss.2007.04.026
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/167447
dc.subjectMachine Learning
dc.subjectArtificial Intelligence
dc.titleBuilding an interpretable fuzzy rule base from data using Orthogonal Least Squares Application to a depollution problem
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