Air quality assessment using Fuzzy Lattice Reasoning (FLR)
Accurate and on-line decision-making is required by decision support systems including those ones used for environmental information management. This paper focuses on air quality assessment and demonstrates the added value of applying data mining techniques in operational decision-making. More specifically, the application of Fuzzy Lattice Reasoning (FLR) classifier is investigated. An enhanced FLR learning algorithm is presented that employs a sigmoid valuation function for introducing tunable non-linearities. The FLR classifier is applied here beyond the unit-hypercube. The FLR with a sigmoid positive valuation function demonstrates an improved performance on a dataset from the region of Valencia, Spain regarding an environmental problem. Descriptive decision making knowledge (i.e. rules) for classification is also induced.
I. N. Athanasiadis, V. G. Kaburlasos, Air quality assessment using Fuzzy Lattice Reasoning (FLR), IEEE Intl Conf on Fuzzy Systems, World Congress on Computational Intelligence (WCCI 2006), pg. 231-236, 2006, IEEE, doi:10.1109/FUZZY.2006.1681690.
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