anirban-part1 - 1 A Survey of Multi-Objective Evolutionary...

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1 A Survey of Multi-Objective Evolutionary Algorithms for Data Mining: Part-I Anirban Mukhopadhyay, Senior Member, IEEE , Ujjwal Maulik, Senior Member, IEEE, Sanghamitra Bandyopadhyay, Senior Member, IEEE , and Carlos A. Coello Coello, Fellow, IEEE Abstract —The aim of any data mining technique is to build an efficient predictive or descriptive model of a large amount of data. Applications of evolutionary algorithms have been found to be particularly useful for automatic processing of large quantities of raw noisy data for optimal parameter setting and to discover significant and meaningful information. Many real-life data mining problems involve multiple conflicting measures of perfor- mance, or objectives, which need to be optimized simultaneously. Under this context, multi-objective evolutionary algorithms are gradually finding more and more applications in the domain of data mining since the beginning of the last decade. In this two- part article, we have made a comprehensive survey on the recent developments of multi-objective evolutionary algorithms for data mining problems. In this Part-I, some basic concepts related to multi-objective optimization and data mining are provided. Subsequently, various multi-objective evolutionary approaches for two major data mining tasks, namely feature selection and classification are surveyed. In Part-II of the article [1], we have surveyed different multi-objective evolutionary algorithms for clustering, association rule mining and several other data mining tasks, and provided a general discussion on the scopes for future research in this domain. Index Terms —Multi-objective evolutionary algorithms, Pareto optimality, feature selection, classification. I. I NTRODUCTION Data mining involves discovering novel, interesting, and potentially useful patterns from large data sets. The objective of any data mining process is to build an efficient predictive or descriptive model of a large amount of data that not only best fits or explains it, but is also able to generalize to new data. It is very important to optimize the model parameters for successful application of any data mining approach. Often such problems, due to their complex nature, cannot be solved using standard mathematical techniques. Moreover, due to the large size of the input data, the problems sometimes become intractable. Therefore, designing efficient deterministic algorithms is often not feasible. Applications of evolutionary algorithms, with their inherent parallel architecture, have been found to be potentially useful for automatic processing of large amounts of raw noisy data for optimal parameter setting and to discover significant and meaningful information [2], [3]. Anirban Mukhopadhyay is with the Department of Computer Science & En- gineering, University of Kalyani, India. Email : anirban @ klyuniv.ac.in Ujjwal Maulik is with the Department of Computer Science & Engineering, Jadavpur University, India. Email : umaulik @ cse.jdvu.ac.in Sanghamitra Bandyopadhyay is with the Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.
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