Dirty Data Processing for Machine Learning

Zhixin Qi is an assistant professor in the School of Transportation Science and Engineering at Harbin Institute of Technology. She received her PhD from the School of Computer Science and Technology at Harbin Institute of Technology. Her research interests include knowledge graph, AI4DB, and graph data management. She has published more than 10 papers in international journals and conferences, including TKDE, KAIS, KBS, Neurocomputing, WWWJ, JCST, CIKM, and DASFAA.Hongzhi Wang is a professor and doctoral supervisor at the School of Computer Science and Technology, Harbin Institute of Technology. His research interests include big data management and analysis, data quality, graph data management, and web data management. He has published more than 150 papers, and he is the primary investigator of more than 10 projects including three NSFC projects, and co-PI of 973, 863, and NSFC key projects. He was awarded as Microsoft fellowship, China Excellent Database Engineer, and IBMPhD fellowship.Zejiao Dong is a full professor in the School of Transportation Science and Engineering of Harbin Institute of Technology. He has served as School Dean and Deputy Director of MOT Key Laboratory of Specialized Material and Intelligent Control for Traffic Safety as well as MIIT Key Laboratory of Intellectualization and Safety Assurance for Cold Regional Transportation Infrastructure. His research interests include intelligent monitoring, massive data analysis, and dynamic mechanics of transportation infrastructures. He has published over 60 research papers in top-quality international journals as well as three academic monographs in national press.

• Chapter 1. Introduction .- Chapter 2. Impacts of Dirty Data on Classification and Clustering Models.- Chapter 3. Dirty-Data Impacts on Regression Models.- Chapter 4. Incomplete Data Classification with View-Based Decision Tree.- Chapter 5. Density-Based Clustering for Incomplete Data.- Chapter 6. Feature Selection on Inconsistent Data.- Chapter 7. Cost-Sensitive Decision Tree Induction on Dirty Data.