Applying machine learning techniques on air quality data for real-time decision support
Fairly rapid environmental changes call for continuous surveillance and decision making, areas where IT technologies can be valuable. In the aforementioned context this work describes the application of a novel classifier, namely greektext σ-FLNMAP, for estimating the ozone concentration level in the atmosphere. In a series of experiments on meteorological and air pollutants data, the greektext σ-FLNMAP classifier compares favorably with both back-propagation neural networks and the C4.5 algorithm; moreover greektext σ-FLNMAP induces only a few rules from the data. The greektext σ-FLNMAP classifier can be implemented as either a neural network or a decision tree. We also discuss the far reaching potential of greektext σ-FLNMAP in IT applications due to its applicability on partially (lattice) ordered data.
I. N. Athanasiadis, V. G. Kaburlasos, P. A. Mitkas, V. Petridis, Applying machine learning techniques on air quality data for real-time decision support, 1st Intl Symp on Information Technologies in Environmental Engineering (ITEE-2003), pg. 51, 2003, ICSC-NAISO Academic Press.
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