Yusuke Nojima | Chun-Hao Chen
Genetic Fuzzy Systems and Its Application to Data Mining
Abstract: The main objectives of data mining are the accuracy and interpretability of the obtained knowledge from databases. How to design sophisticated algorithms to deal with them is an attractive issue for researches. In recent years, many statistical methods have actively been studied to pursue more and more accurate knowledge. However, the interpretability of such knowledge is often low because of its complexity. On the contrary, symbolic methods have also been studied to obtain interpretable knowledge for further analyses. One of the promising methods is fuzzy data mining which can obtain linguistically interpretable fuzzy if-then rules from databases. To enhance the generalization ability for unseen data, the interpretability for users, and the applicability to real-world problems, evolutionary computation has successfully been incorporated into fuzzy data mining. This approach is often referred to as genetic fuzzy systems. In this tutorial, we introduce the history of genetic fuzzy systems and basic taxonomy. Then we explain two main streams in genetic fuzzy systems: classification and association rule mining. Some current topics such as multiobjective optimization and parallel implementation are also explained.
Yusuke Nojima received his Doctor of Engineering Degree from Kobe University, Japan in 2004. He joined Osaka Prefecture University, Japan, as an assistant professor and is currently an associate professor from 2013. His research interests include genetic fuzzy systems, fuzzy data mining, and evolutionary multi-objective optimization. He has published more than 200 research articles in book chapters, international journals, and international conferences. His h-index = 21 since 2010. He was guest editors for five special issues in international journals. He is currently a task force chair on Evolutionary Fuzzy Systems in Fuzzy Systems Technical Committee of IEEE Computational Intelligence Society. He is an associate editor of IEEE Computational Intelligence Magazine. He is also a program vice-chair of 2017 IFSA world congress.
Chun-Hao Chen is an Associate Professor at Department of Computer Science and Information Engineering at Tamkang University, Taiwan. Dr. Chen received his Ph.D. degree with major in computer science and information engineering from National Cheng Kung University, Taiwan, in 2008. During February 2010 to August 2013, he served as an assistant professor of Department of Computer Science and Information Engineering at Tamkang University. He has a wide variety of research interests covering data mining, database systems, time series, machine learning, evolutionary algorithms, and fuzzy theory. He has published more than 75 research papers in referred journals and international conferences. He has served as reviewer of a number of journals including IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Fuzzy Systems, and Knowledge-Based Systems.