An experimental evaluation of ZCS-DM for the prediction of urban air quality

Understanding and forecasting urban Air Quality (AQ) is not only a multifaceted 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. Unlike neural network or regression-based techniques traditionally used in the domain of AQ, our current approach adopts ZCS-DM – Zeroth-level Classifier System for Data Mining – for the production of a set of AQ prediction rules in the urban domain. On this basis, the aim of our experimental investigation is twofold and includes (i) the handling of incomplete data matrices and (ii) the evaluation of ZCS-DM effectiveness against methods widely used in the target domain, namely multi-layer perceptron (MLP), support vector machines (SVM), linear discriminant analysis (LDA) and classification trees (C4.5). Overall, the obtained results reveal the potential of ZCS-DM as a data mining tool to be used for AQ forecasting, and point to its insensitivity for missing values (cf. MLP, SVM, LDA) and the understandability of produced models as its greater advantages.