The 5th International Conference on Modern Management based on Big Data (MMBD2024)

Abstract: The correct selection of knowledge elements is the key to emergency management. Using emergency knowledge elements, this study constructs an assertive reasoning selection methodology by improving acquisition on the balance coefficient in the C4.5 algorithm. Through hierarchical representation, a two level model selection method, based on a top model construction process as well as an underlying model selection process, is proposed. Specifically, the top model construction process is based on assertive reasoning and an underlying model selection process is based on the C4.5 decision tree. This method reduces the requirements for emergency domain knowledge, and improves the accuracy and timeliness. Lastly, the methodology is applied to the 2015 Tianjin explosions as a case study.

Biography: Dr. Lu Han is Associate Professor and Master Tutor in the School of Management Science and Engineering at Central University of Finance and Economics. She worked as visiting scholar in the School of Information and Library Science at University of North Carolina Chapel Hill (USA) from September 2018 to September 2019, and she was engaged in postdoctoral research in the PBC School of Finance at Tsinghua University from July 2012 to June 2014. She received doctor degree in the specialty of Management Science and Engineering in the School of Economics and Management at Beihang University in 2012, and she received bachelor’s degree in the specialty of Information Management and Information System in the School of Economics and Management at Beihang University in 2007.

She teaches courses such as "Knowledge Management", "Decision Theories and Methods", "Mathematical Modeling", "Basis of MATLAB and Modeling", "Credit Investigation with Big Data" and so on.

Her research interests include Knowledge Engineering, Data Mining, Credit Management and M&A.