Abstract
Ranking and scoring have been widely used for a period of time in scientific discovery, technology innovation, and social evolution. More recently, ranking, scoring, and combination of multiple scoring systems contribute tremendously to several disciplines such as STEM, social science, data science, and other professional studies in law, business and education.
In this talk, I will cover methods and practices for combining multiple scoring systems (MSS’). Each of the scoring systems can exist in two different levels of context: (1) as a variable (feature, parameter, cue, indicator, etc.), or (2) as a system (regression, forecasting, classification, neural net, model, decision, data mining, machine learning, etc.). In particular, we explore the issues of “when” and “how” to combine these MSS’. Conventional wisdom is that “The combination of two (or more) systems can be better than each individual system only if they are relatively good and they are diverse”. However, measurement of diversity is a challenging issue in Big Data analytics as well as in micro- and macro-informatics.
The notion of a Cognitive Diversity (CD) will be introduced. “Cognitive diversity” measures diversity between two information systems as opposed to “statistical correlation”, which measures correlation between two data distributions. CD is useful because it is simple to compute and it is independent of the data items. Based on cognitive diversity, we perform variable (or system) selection and combination. Examples are drawn from domain applications in information retrieval, target tracking, joint decision making, ChIP-seq analysis, virtual screening, cognitive neuroscience, wireless network selection, and portfolio management. We will also discuss the application of combining MSS to various problems in cybersecurity, risk management, and decision support systems.
Speaker Bio
Dr Frank Hsu is the Clavius Distinguished Professor of Science, a professor of computer and information science, and director of the Laboratory of Informatics and Data Mining at Fordham University, New York, NY. He has held visiting positions at CNRS (and University of Paris-Sud), JAIST (as Komatsu Chair Professor, Kanazawa, Japan), Keio University (as IBM Chair Professor, Tokyo, Japan), MIT (Applied math and Laboratory of Computer Science), National Taiwan University, and National Tsing-Hua University (in Hsin-Chu, Taiwan). He was also chair of the computer and information science at the New York Academy of Science.
Hsu’s current research interests include Big Data analytics, interconnection networks, machine learning, combinatorial fusion, and macro-informatics. The Combinatorial Fusion algorithm he and colleagues proposed in 2005 has been applied to diverse areas such as bioinformatics, finance, target tracking, virtual screening (and drug discovery), decision making, and cognitive neuroscience.
Hsu received a BA from National Cheng Kung University in Taiwan, an MA from the University of Texas at El Paso, and a PhD from the University of Michigan. He has served on many editorial boards including IEEE Transactions on Computers, IEEE Transaction on Reliability, Networks, Pattern Recognition Letter, International Journal of Foundation of Computer Science, and JOIN (Journal of Interconnection Networks). He has served as co-chairs of many conferences, workshops, PC’s and steering committees including DIMACS Workshops and I-SPAN (14th I-SPAN’ 2017 at Exeter, UK). Hsu received the Best Paper Award in 2005 at the IEEE-AINA Conference and in 2013 at the Brain and Health Informatics Conference. He received an IBM Faulty Award in 2012. Dr. Hsu is a Fellow of the New York Academy of Sciences and the International Institute of Cognitive Informatics and Cognitive Computing (ICIC). He is a Foundation Fellow of the Institute of Combinatorics and Application (ICA) and a Senior member of the IEEE.
Registration
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