Optimization techniques have been widely adopted to implement various data mining algorithms. This book focuses on cutting-edge theoretical developments and real-life applications in optimization, covering a range of fields from finance to bioinformatics.

Introduces MCLP for data mining intuitively, systemically and comprehensively Offers classification problems and regression problems which are the two main components of data mining Constructs SVM's for solving multi-class classification problems Includes supplementary material: sn.pub/extras

Autorentext

Dr. Gang Kou is a professor of School of Management and Economics, University of Electronic Science and Technology of China and managing editor of International Journal of Information Technology & Decision Making. Previously, he was a research scientist in Thomson Co., R&D. He received his Ph.D. in Information Technology from the College of Information Science & Technology, Univ. of Nebraska at Omaha; got his Master degree in Dept of Computer Science, Univ. of Nebraska at Omaha; and B.S. degree in Department of Physics, Tsinghua University, Beijing, China. He has published more than eighty papers in various peer-reviewed journals and conferences. Gang Kou has been Keynote speaker/workshop chair in several international conferences. He co-chaired Data Mining contest on The Seventh IEEE International Conference on Data Mining 2007 and he is the Program Committee Co-Chair of the 20th International Conference on Multiple Criteria Decision Making (2009) and NCM 2009: 5th International Joint Conference on INC, ICM and IDC. He is also co-editor of special issues of several journals, such as Journal of Multi Criteria Decision Analysis, Decision Support Systems, Journal of Supercomputing and Information Sciences. He accomplished more than 300 cites of published journal articles as shown in the Science Citation Index (SCI) database. Daji Ergu, a PhD candidate in Management Science and Engineering, College of Economics & Management, University of electronic science and technology of China. He has been a lecturer of Engineering Mathematics, College of Electrical Information & Technology, Southwest University for Nationalities since 2003. Ergu's research interests include multiple criteria decision making, risk analysis and data mining. He has published 9 papers, and 4 of which collected in SCI/EI Indexes. Dr. Yi Peng is a Professor of School of Management and Economics, Universityof Electronic Science and Technology of China. Previously, she worked as Senior Analyst for West Co., USA. Dr. Peng received her Ph.D. in Information Technology from the College of Information Science & Technology, Univ. of Nebraska at Omaha and got her Master degree in Dept of Info. Science & Quality Assurance, Univ. of Nebraska at Omaha and B.S. degree in Department of Management Information Systems, Sichuan University, China. Dr. Peng's research interests cover Knowledge Discover in Database and data mining, multi-criteria decision making, data mining methods and modeling, knowledge discovery in real-life applications. She published more than sity papers in various peer-reviewed journals and conferences. She is the Workshop Chair of the 20th International Conference on Multiple Criteria Decision Making (2009), guest editor of Annals of Operations Research's special issue on Multiple Criteria Decision Making on Operations Research. Dr. Yong Shi, Senior Member of IEEE, serves as the Executive Deputy Director, Chinese Academy of Sciences Research Center on Fictitious Economy & Data Science. He has been the Charles W. and Margre H. Durham Distinguished Professor of Information Technology, College of Information Science and Technology, Peter Kiewit Institute, University of Nebraska, USA since 1999. Dr. Shi's research interests include business intelligence, data mining, and multiple criteria decision making. He has published more than 17 books, over 200 papers in various journals and numerous conferences/proceedings papers. He is the Editor-in-Chief of International Journal of Information Technology and Decision Making (SCI), and a member of Editorial Board for a number of academic journals. Dr. Shi has received many distinguished awards including the Georg Cantor Award of the International Society on Multiple Criteria Decision Making (MCDM), 2009; Fudan Prize of Distinguished Contribution in Management, Fudan Premium Fund ofManagement, China, 2009; Outstanding Young Scientist Award, National Natural Science Foundation of Chin

Inhalt

Support Vector Machines for Classification Problems.- Method of Maximum Margin.-Dual Problem.- Soft Margin.- C- Support Vector Classification.-C- Support Vector Classification with Nominal Attributes.- LOO Bounds for Support Vector Machines.-Introduction.- LOO bounds for Support Vector Regression.- LOO Bounds for Support Vector Ordinal Regression Machine .- Support Vector Machines for Multi-class Classification Problems.-K-class Linear Programming Support Vector Classification Regression Machine (KLPSVCR).-Support Vector Ordinal Regression Machine for Multi-class Problems.- Unsupervised and Semi-Supervised Support Vector Machines.- Unsupervised and Semi-Supervised -Support Vector Machine.- Numerical Experiments.-Unsupervised and Semi-supervised Lagrange Support Vector Machine.-Unconstrained Transductive Support Vector Machine.-Robust Support Vector Machines.-Support Vector Ordinal Regression Machine.- Robust Multi-class Algorithm.- Robust Unsupervised and Semi-Supervised Bounded C-Support Vector Machine.-Feature Selection via lp-norm Support Vector Machines.-lp-norm Support Vector Classification.-lp-norm Proximal Support Vector Machine.-Multiple Criteria Linear Programming.-Comparison of Support Vector Machine and Multiple Criteria Programming.-Multiple Criteria Linear Programming.-Multiple Criteria Linear Programming for Multiple Classes.- Penalized Multiple Criteria Linear Programming.-Regularized Multiple Criteria Linear Programs for Classification.-MCLP Extensions.- Fuzzy MCLP.-FMCLP with Soft Constraints.-FMCLP by Tolerances.-Kernel based MCLP.- Knowledge based MCLP.- Rough set based MCLP.- Regression by MCLP.-Multiple Criteria Quadratic Programming.-A General Multiple Mathematical Programming.- Multi-criteria Convex Quadratic Programming Model Kernel based MCQP.- Non-additiveMCLP.-Non-additiveMeasures and Integrals.-Non-additive Classification Models.-Non-additive MCP.- Reducing the time complexity.-Hierarchical Choquet integral.-Choquetintegral with respect to k-additive measure.-MC2LP.-MC2LP Classification.-Minimal Error and Maximal Between-class Variance Model.-Firm Financial Analysis.-Finance and Banking.- General Classification Process.-Firm Bankruptcy Prediction.- Personal Credit Management.- Credit Card Accounts Classification.- Two-class Analysis.-FMCLP Analysis.- Three-class Analysis.- Four-class Analysis.-Empirical Study and Managerial Significance of Four-class Models.- Health Insurance Fraud Detection.- Problem Identification.- A Real-life Data Mining Study.- Network Intrusion Detection.- Problem and Two Datasets.- Classify NeWT Lab Data by MCMP, MCMP with kernel and See5.- Classify KDDCUP-Data by Nine Different Methods.- Internet Service Analysis.- VIP Mail Dataset.- Empirical Study of Cross-validation.-Comparison of Multiple-Criteria Programming Models and SVM.-HIV-1 Informatics.- HIV-1 Mediated Neuronal Dendritic and Synaptic Damage.- Materials and Methods.- Designs of Classifications.- Analytic Results.- Anti-gen and Anti-body Informatics.- Problem Background.- MCQP,LDA and DT Analyses.-Kernel-based MCQP and SVM Analyses.-Geol-chemical Analyses.-Problem Description.- Multiple-class Analyses.- More Advanced Analyses.-Intelligent Knowledge Management.- Purposes of the Study.- Definitions and Theoretical Framework of Intelligent Knowledge.-Some Research Directions.