Hsinchun Chen

Hsinchun Chen

Member of the Graduate Faculty
Professor, BIO5 Institute
Professor, Management Information Systems
Regents Professor
Primary Department
Contact
(520) 621-4153

Research Interest

Research Interest
Dr Chen's areas of expertise include:Security informatics, security big data; smart and connected health, health analytics; data, text, web mining.Digital library, intelligent information retrieval, automatic categorization and classification, machine learning for IR, large-scale information analysis and visualization.Internet resource discovery, digital libraries, IR for large-scale scientific and business databases, customized IR, multilingual IR.Knowledge-based systems design, knowledge discovery in databases, hypertext systems, machine learning, neural networks computing, genetic algorithms, simulated annealing.Cognitive modeling, human-computer interactions, IR behaviors, human problem-solving process.

Publications

Schumaker, R. P., & Chen, H. (2008). Evaluating a news-aware quantitative trader: The effect of momentum and contrarian stock selection strategies. Journal of the American Society for Information Science and Technology, 59(2), 247-255.

Abstract:

We study the coupling of basic quantitative portfolio selection strategies with a financial news article prediction system, AZFinText. By varying the degrees of portfolio formation time, we found that a hybrid system using both quantitative strategy and a full set of financial news articles performed the best. With a 1-week portfolio formation period, we achieved a 20.79% trading return using a Momentum strategy and a 4.54% return using a Contrarian strategy over a 5-week holding period. We also found that trader overreaction to these events led AZFinText to capitalize on these short-term surges in price.

Chen, H. (1992). Knowledge-based document retrieval: Framework and design. Journal of Information Science, 18(4), 293-314.

Abstract:

This article presents research on the design of knowledge-based document retrieval systems. We adopted a semantic network structure to represent subject knowledge and classification scheme knowledge and modeled experts' search strategies and user modeling capability as procedural knowledge. These functionalities were incorporated into a prototype knowledge-based retrieval system, Metacat. Our system, the design of which was based on the blackboard architecture, was able to create a user profile, identify task requirements, suggest heuristics-based search strategies, perform semantic-based search assistance, and assist online query refinement.

Chen, H., & Chau, M. (2004). Web Mining: Machine Learning for Web Applications. Annual Review of Information Science and Technology, 38, 289-329+xvii-xviii.
Jeixun, L. i., Hua, S. u., Chen, H., & Futscher, B. W. (2007). Optimal search-based gene subset selection for gene array cancer classification. IEEE Transactions on Information Technology in Biomedicine, 11(4), 398-405.
BIO5 Collaborators
Hsinchun Chen, Bernard W Futscher

PMID: 17674622;Abstract:

High dimensionality has been a major problem for gene array-based cancer classification. It is critical to identify marker genes for cancer diagnoses. We developed a framework of gene selection methods based on previous studies. This paper focuses on optimal search-based subset selection methods because they evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this paper is the first to introduce tabu search (TS) to gene selection from high-dimensional gene array data. Our comparative study of gene selection methods demonstrated the effectiveness of optimal search-based gene subset selection to identify cancer marker genes. TS was shown to be a promising tool for gene subset selection. © 2007 IEEE.

Chen, H., & Zhang, Y. (2011). Trends and controversies. IEEE Intelligent Systems, 26(1), 80-89.

Abstract:

The rich social media data generated in virtual worlds has important implications for business, education, social science, and society at large. Similarly, massively multiplayer online games (MMOGs) have become increasingly popular and have online communities comprising tens of millions of players. They serve as unprecedented tools for theorizing about and empirically modeling the social and behavioral dynamics of individuals, groups, and networks within large communities. Some technologists consider virtual worlds and MMOGs to be likely candidates to become the Web 3.0. AI can play a significant role, from multiagent avatar research and immersive virtual interface design to virtual world and MMOG Web mining and computational social science modeling. This issue includes articles with research examples from distinguished experts in social science and computer science. Each article presents a unique research framework, computational methods, and selected results. © 2011 IEEE.