Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation

• Autores: Vassilis Kaburlasos, Ioannis Athanasiadis, Pericles Mitkas
• Localización: International journal of approximate reasoning, ISSN 0888-613X, Vol. 45, Nº 1, 2007, págs. 152-188
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • The fuzzy lattice reasoning (FLR) classifier is presented for inducing descriptive, decision-making knowledge (rules) in a mathematical lattice data domain including space RN. Tunable generalization is possible based on non-linear (sigmoid) positive valuation functions; moreover, the FLR classifier can deal with missing data. Learning is carried out both incrementally and fast by computing disjunctions of join-lattice interval conjunctions, where a join-lattice interval conjunction corresponds to a hyperbox in RN. Our testbed in this work concerns the problem of estimating ambient ozone concentration from both meteorological and air-pollutant measurements. The results compare favorably with results obtained by C4.5 decision trees, fuzzy-ART as well as back-propagation neural networks. Novelties and advantages of classifier FLR are detailed extensively and in comparison with related work from the literature