The fuzzy lattice reasoning (FLR) classifier is presented for inducing descriptive, decision-making knowledge (rules) in a mathematical lattice data domain including space R N . 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 R N . 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 0888-613X/$ -see front matter Ó
More recently, a fuzzy lattice reasoning classifier REF was proposed to induce rules in a mathematical lattice data domain.
Fuzzy lattice reasoning (FLR) classifier and its application for ambient ozone estimation
Int. J. Approx. Reason.
Int. J. Approx. Reason.
Computer Science Mathematics