Fuzzy Lattice Reasoning (FLR) classifier and its application for ambient ozone estimation
Abstract
The Fuzzy Lattice Reasoning (FLR) classifier is presented for inducing descriptive, decision-making knowledge (rules) in a mathematical lattice data domain including the Euclidean space. 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 Euclidean space. 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 backpropagation neural networks. Novelties and advantages of classifier FLR are detailed extensively and in comparison with related work from the literature.
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Published as:
V. G. Kaburlasos,
I. N. Athanasiadis,
P. A. Mitkas,
Fuzzy Lattice Reasoning (FLR) classifier and its application for ambient ozone estimation,
International Journal of Approximate Reasoning, 45:152-188,
2007, doi:10.1016/j.ijar.2006.08.001.
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