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Traditionally, modelling tasks involve the building of mathematical equations which can best describe the underlying process. Such a modelling practice normally requires a deep understanding of the systems under investigation, hence the reason why it is often referred to as knowledge-driven modelling. On the contrary, knowledge extraction from data (or datadriven modelling), inspired principally from artificial intelligence techniques, is based on limited knowledge of the modelling process and relies on the data describing the input and output mappings. Such a process is able to make abstractions and generalisations of the process and plays often a complementary role to knowledge-driven modelling. The Fuzzy Rule-Based System (FRBS) has been found more appealing for such a knowledge extraction process, compared to other ‘black-box’ modelling techniques, due to its ability of providing human understandable knowledge. However, such interpretability is only semiinherent in the FRBS. Without a special caution one can easily end up with a FRBS with equally good predictions as those given by the ‘black-box’ modelling methods, while on the other hand with equally bad interpretability. Hence, extracting a transparent (interpretative) FRBS is reckoned to be of a multi-objective nature with often conflicting outcomes, which gives the rationale of using bio-inspired optimisation paradigms, more specifically, Artificial Immune Systems, in this research project. In a bid to further improve the overall predictive performance, especially for the scatter and uncertain data set, an error correction scheme is proposed so that one can compensate the …