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Meta-Learning in Computational Intelligence (Studies in Computational Intelligence)

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In the early days of pattern recognition and statistical data analysis life was rather simple: datasets were relatively small, collected from well-designed experiments, analyzed using a few methods that had good theoretical background. Explosive growth of the use of computers led to the creation of huge amounts of data of all kinds, coming from business, finance, scientific experiments, medical evaluations, Web-related data, multimedia, various imaging techniques, sensor networks, and many others sources. Human experts are not capable of deep exploration of large amounts of data, especially when an expertize in several different areas is necessary for this purpose.

The need for scalable algorithms applicable for massive data sets, discovering novel patterns in data, contributed to the growing interest in data mining and knowledge discovery. It led to the development of new machine learning techniques, including the use of inspirations from nature to develop evolutionary, neural and fuzzy algorithms for data analysis and understanding. Computational intelligence became a field in its own, combining all these methods, and the number of algorithms available for data analysis was rapidly growing. These algorithms started to be collected in larger data mining packages, with a free package called Weka starting the trend, and many others that soon followed. These packages added better user interfaces, environments to build data flow schemes by connecting various modules and test various algorithms. However, the number of modules at each stage: pre-processing, data acquisition, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing became unmanageably large. A large data mining package allows for billions of combinations, making the process of knowledge discovery increasingly difficult. Gone are the days when the life of a data miner was simple and a background course in multivariate statistics was all that was needed to do the job.

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