C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
An improved back propagation algorithm topredict episodes of poor air quality
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Learning classifier systems: a survey
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Comparison of classification accuracy using Cohen's Weighted Kappa
Expert Systems with Applications: An International Journal
Zcs: A zeroth level classifier system
Evolutionary Computation
ZCS Revisited: Zeroth-Level Classifier Systems for Data Mining
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
PM10 forecasting for Thessaloniki, Greece
Environmental Modelling & Software
Engineering Applications of Artificial Intelligence
Hi-index | 12.05 |
Sparse episode identification in environmental datasets is not only a multi-faceted and computationally challenging problem for machine learning algorithms, but also a difficult task for human-decision makers: the strict regulatory framework, in combination with the public demand for better information services, poses the need for robust, efficient and, more importantly, understandable forecasting models. Additionally, these models need to provide decision-makers with ''summarized'' and valuable knowledge, that has to be subjected to a thorough evaluation procedure, easily translated to services and/or actions in actual decision making situations, and integratable with existing Environmental Management Systems (EMSs). On this basis, our current study investigates the potential of various machine learning algorithms as tools for air quality (AQ) episode forecasting and assesses them - given the corresponding domain-specific requirements - using an evaluation procedure, tailored to the task at hand. Among the algorithms employed in the experimental phase, our main focus is on ZCS-DM, an evolutionary rule-induction algorithm specifically designed to tackle this class of problems - that is classification problems with skewed class distributions, where cost-sensitive model building is required. Overall, we consider this investigation successful, in terms of its aforementioned goals and constraints: obtained experimental results reveal the potential of rule-based algorithms for urban AQ forecasting, and point towards ZCS-DM as the most suitable algorithm for the target domain, providing the best trade-off between model performance and understandability.