Journal
APPLIED SOFT COMPUTING
Volume 24, Issue -, Pages 135-141Publisher
ELSEVIER
DOI: 10.1016/j.asoc.2014.07.008
Keywords
Algorithms; Artificial neural networks; Learning machine; Ozone prediction; Multilayer perceptron; Support vector machine
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Funding
- The Hong Kong Research Grant Council, RGC-GRF Grant [CityU 18212]
- City University of Hong Kong, Strategic Research Grant [SRG 7002718]
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Multilayer perceptron (MLP) and support vector machine (SVM), two popular learning machines, are increasingly being used as alternatives to classical statistical models for ground-level ozone prediction. However, employing learning machines without sufficient awareness about their limitations can lead to unsatisfactory results in modeling the ozone evolving mechanism, especially during ozone formation episodes. With the spirit of literature review and justification, this paper discusses, with respect to the concerning of ozone prediction, the recently developed algorithms/technologies for treating the most prominent model-performance-degradation limitations. MLP has the black-box property, i.e., it hardly provides physical explanation for the trained model, overfitting and local minima problems, and SVM has parameters identification and class imbalance problems. This commentary article aims to stress that the underlying philosophy of using learning machines is by no means as trivial as simply fitting models to the data because it causes difficulties, controversies or unresolved problems. This article also aims to serve as a reference point for further technical readings for experts in relevant fields. (C) 2014 Elsevier B.V. All rights reserved.
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