Paper Info
Title
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 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 networks. Novelties and advantages of classifier FLR are detailed extensively and in comparison with related work from the literature.
Year
DOI
Venue
2007
10.1016/j.ijar.2006.08.001
Int. J. Approx. Reasoning
Keywords
Field
DocType
mathematical lattice data domain,ambient ozone estimation,join-lattice interval conjunction corresponds,fuzzy lattice reasoning (flr),fuzzy lattice reasoning,join-lattice interval conjunction,n. tunable generalization,space r,classification,classifier flr,related work,flr classifier,machine learning,missing data,missing values,decision tree,ozone
Decision tree,Data domain,Fuzzy logic,Algorithm,Artificial intelligence,Missing data,Backpropagation,Artificial neural network,Classifier (linguistics),Mathematics,Machine learning,Sigmoid function
Journal
Volume
Issue
ISSN
45
1
International Journal of Approximate Reasoning
Citations 
PageRank 
References 
56
2.48
84
Authors
3
Name
Order
Citations
PageRank
Vassilis G. Kaburlasos156538.74
Ioannis N. Athanasiadis235244.44
PERICLES A. MITKAS373379.29