Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study. (English) Zbl 1107.68450
Summary: Fuzzy ordered classifiers were used to assign fuzzy labels to river sites expressing their suitability as a habitat for a certain macroinvertebrate taxon, given up to three abiotic properties of the considered river site. The models were built using expert knowledge and evaluated on data collected in the Province of Overijssel in the Netherlands. Apart from a performance measure for crisp classifiers common in the aquatic ecology domain, the percentage of correctly classified instances (% CCI), two performance measures for fuzzy (ordered) classifiers are introduced in this paper: the percentage of correctly fuzzy classified instances (% CFCI) and the average deviation (AD). Furthermore, results of an interpretability-preserving genetic optimization of the linguistic terms, applying once binary encoding and once real encoding, are presented.
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 68T10 | Pattern recognition, speech recognition |
| 68T20 | Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) |
| 92D40 | Ecology |
Keywords:
habitat suitability models; fuzzy ordered classifiers; linguistic fuzzy models; genetic algorithms; interpretabilitySoftware:
GenocopReferences:
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