Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases (Studies in Computational Intelligence, 98, Band 98) - Hardcover

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Ashish Ghosh is the Director of the International Institute of Information Technology Bhubaneswar, India, on deputation from the Indian Statistical Institute (ISI) Kolkata. He began as an electronic engineer, transitioned to computer science for his master s, and focused on artificial intelligence (AI) for his PhD. After postdoctoral research in Japan, he joined ISI Kolkata as a faculty member and is now a Senior Professor there. Ghosh has made significant contributions to AI, machine learning, image/video analysis, and data science, publishing over 300 research articles, editing 12 books, and achieving an h-index of 53. He ranks among the top 2% of scientists in computer science.Ashish Ghosh was part of the founding team for the National Center for Soft Computing Research at ISI Kolkata in 2004, funded by the Department of Science and Technology (DST), Government of India. He served as In-Charge of the Center, now recognized as an Associate Institute of ISI Kolkata, and led the Machine Intelligence Unit. Currently, he heads the Data Science Research Consortium Project under a national mission of DST and established the Technology Innovation Hub on Data Science, Big Data Analytics, and Data Curation at ISI Kolkata, funded with INR 100 Crores under the NMICPS mission.For his contributions to computational intelligence and image analysis, Ghosh received the Young Scientist Award from the Indian Science Congress Association (ISCA, 1992), the Young Scientist Medal from the Indian National Science Academy (INSA, 1995), and the Young Associateship of the Indian Academy of Sciences (IASc, 1997). He is a Fellow of the West Bengal Academy of Science and Technology (WAST, 2015), the National Academy of Sciences (NASI, 2019), the International Association for Pattern Recognition (IAPR, 2020), the Asia-Pacific Artificial Intelligence Association (AAIA, 2022), and the International Artificial Intelligence Industry Alliance (AIIA, 2023). He received the IEEE-GRSS Regional Leader Award in 2019 for his service to IEEE and is a member of various Research Advisory Committees and the Academic Council of Banasthali Vidyapith.Ghosh is an Associate Editor for several international journals, including the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IET Journal of Computer Vision, and Neural Networks. He has delivered lectures and conducted collaborative research in countries such as China, Germany, Japan, and the USA, and has served as a plenary/keynote speaker at numerous international conferences. As PI and Co-PI, he has completed 18 significant projects funded by the Government of India, European Commission, and Indo-Italy and Indo-US forums for science and technology.

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Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM.

The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.