Fuzzy Lattice Reasoning (FLR) classifier and its application for ambient ozone estimation
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.
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.
You might also enjoy (View all publications)
- Defining and classifying infrastructural contestation: Towards a synergy between anthropology and data science
- Investigation of common big data analytics and decision-making requirements across diverse precision agriculture and livestock farming use cases
- Blockchain applications in the agrifood domain: the first wave