Recently published journal publications - January and February 2012 (Expired)

The Department of Computer Science is pleased to announce the recent publication of seven journal papers by members of the department.

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Nouman Azam and JingTao Yao, Comparison of Term Frequency and Document Frequency Based Feature Selection Metrics in Text Categorization, Expert Systems with Applications, 39(5), 4760-4768, 2012. The Expert Systems with Applications journal ranks the third worldwide for intelligent systems/AI-related journals, according to the most recent Thomson impact factors.  For more information on the journal, please see http://www.journals.elsevier.com/expert-systems-with-applications/.

Nouman Azam is a Ph.D. candidate in our department and JingTao Yao is a faculty member in our department.

Abstract:

Text categorization plays an important role in applications where information is filtered, monitored, personalized, categorized, organized or searched. Feature selection remains as an effective and efficient technique in text categorization. Feature selection metrics are commonly based on term
frequency or document frequency of a word. We focus on relative importance of these frequencies for feature selection metrics. The document frequency based metrics of discriminative power measure and GINI index were examined with term frequency for this purpose. The metrics were compared and analyzed on Reuters 21,578 dataset. Experimental results revealed that the term frequency based metrics may be useful especially for smaller feature sets. Two characteristics of term frequency based metrics were observed by analyzing the scatter of features among classes and the rate at which information in data was covered. These characteristics may contribute toward their superior performance for smaller feature sets.

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Dosselmann, R. and X.D. Yang, A comprehensive assessment of the structural similarity index. Signal, Image and Video Processing, 5(1): 81-91, 2011.  For more information on this journal, see: http://www.springer.com/engineering/signals/journal/11760

Richard Dosselmann is a Ph.D. candidate in our department and Xue Dong Yang is the head of our department.

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Wei Jiang (MSc student) and Samira Sadaoui, Evaluating and Ranking Semantic Offers According to Users' Interests.  Journal of Electronic Commerce Research (JECR), Volume 13, Number 1,
pp 1-22, Feb. 2012. 

As stated on the JECR website (http://www.csulb.edu/journals/jecr/c_i.htm):  "A study by Bharati and Tarasewich published in the May, 2002, issue of the Communications of the ACM ranks the Journal of Electronic Commerce Research fourth in overall quality in publishing E-Commerce research".

Wei Jiang is an MSc student and Samira Sadaoui is a faculty member in our department.

Abstract:

Semantic matchmaking systems are not successful in identifying the differences between users? interests. To address this weakness, we develop a user-oriented and personalized system to evaluate the offers that match the user?s request. Our system evaluates and ranks the offers according to the user?s specific interests to bring better results to each individual. The best offer represents the maximum satisfaction of the user. The proposed system extracts and analyzes
the user?s interests for multiple offer attributes. To evaluate the offers, we adapt the well-known economic model MultiNomial Logit to the field of semantic matchmaking. We show the benefits of our offer evaluation system through a detailed case study involving multiple, high-dimensional, concept and value-based attributes. In this study, we show how our system catches the differences between the purchasing interests of two buyers, and how it recommends a different best offer to each buyer. Furthermore, we assess the feasibility of the proposed system with a transport usage dataset. The experiment results demonstrate that our system can provide good result for each individual.

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S. Wang, G.H. Huang, and B.T. Yang, An interval-valued fuzzy-stochastic programming approach and its application to municipal solid waste management, Environmental Modelling & Software,
29(1), 24-36, 2012. 

Dr. Huang is a faculty member in Engineering and Boting Yang is a faculty member in our department.

Abstract:

In this study, an interval-valued fuzzy-stochastic programming (IVFSP) approach is developed for
municipal solid waste (MSW) management under uncertainty. IVFSP can tackle multiple uncertainties presented as intervals as well as possibilistic and probabilistic distributions. The adoption of interval-valued fuzzy sets is capable of reflecting waste managers' confidence levels over subjective judgments, and can thus enhance the system robustness. An infinite alpha-cuts method is employed for discretizing the interval-valued fuzzy sets in IVFSP. Such a method can communicate all fuzzy information into the optimization process without ignoring valuable uncertain information. Moreover, IVFSP can permit indepth analyses of various policy scenarios that are associated with different levels of economic penalties when the promised waste-allocation targets are violated. The developed approach is applied to a MSW management problem to demonstrate its applicability. The results indicate that interval solutions associated with different risk levels of constraint violation have been generated. They can help waste managers to identify desired waste-flow-allocation schemes and capacity-expansion plans according to their preference and practical conditions, as well as facilitate in-depth analyses of tradeoffs between economic efficiency and constraint-violation risk.

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Wang, X., Rostoker, C., and Hamilton, H.J., A Density-Based Spatial Clustering Method for Physical Constraints,' Journal of Intelligent Information Systems, 38(1), 269-297, 2012. DOI:10.1007/s10844-011-0154-7.  See http://www.springerlink.com/content/b774538370q4w115/

Abstract:

We propose a spatial clustering method, called DBRS+, which aims to cluster spatial data in the presence of both obstacles and facilitators. It can handle datasets with intersected obstacles and facilitators. Without preprocessing, DBRS+ processes constraints during clustering. It can find clusters with arbitrary shapes. DBRS+ has been empirically evaluated using synthetic and real data sets and its performance has been compared to DBRS and three related methods for handling obstacles, namely AUTOCLUST+, DBCLuC*, and DBRS_O.

Xin Wang is a Ph.D. graduate of our department who is now a professor at the University of Calgary, Camilo Rostoker was an undergraduate student holding an Undergraduate Student Research Award in our department, and Howard Hamilton is a faculty member in our department.

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Xiaoping Yang and JingTao Yao, Modelling Multi-agent three-way Decisions
with Decision-theoretic Rough Sets, Fundamenta Informaticae, 115(2):157-171, 2012.  For more information on the journal, please see http://fi.mimuw.edu.pl/index.php/FI

Professor Xiaoping Yang was a visiting scholar from Zhejiang Ocean University in China. He was with us from Sept 2009 to Sept 2009.  JingTao Yao is a faculty member in our department.

Abstract:

The decision-theoretic rough set (DTRS) model considers costs associated with actions of classifying an equivalence class into a particular region. With DTRS, one may make informative decisions in the form of three-way decisions. Current research mainly focuses on single agent DTRS which is too complex for making a decision when multiple agents are involved. We propose
a multi-agent DTRS model and express it in the form of three-way decisions.  The new model seeks for synthesized or consensus decisions when there are multiple decision preferences and criteria adopted by different agents. Various multi-agent DTRS models can be derived according to the conservative, aggressive and majority viewpoints based on the positive, negative and
boundary regions made by each agent. These multi-agent decision regions are expressed by figures in the form of three-way decisions.

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Yiyu Yao, Nan Zhang, Duoqian Miao, and Feifei Xu, Set-theoretic Approaches to Granular Computing  Fundamenta Informaticae 115 (2012), 247-264.  DOI 10.3233/FI-2012-653.
 
Dr. Yiyu Yao is a faculty member in our department. Dr. Miao was a visiting scholar in our department in 2008 and he is currently Vice-Dean, School of Electronics and Information Engineering, Tongji University, China.  Dr. Xu and Dr. Zhang were visiting research students in our department in 2008 and 2010, respectively.

Abstract:

A framework is proposed for studying a particular class of set-theoretic approaches to granular computing. A granule is a subset of a universal set, a granular structure is a family of subsets of the universal set, and relationship between granules is given by the standard set-inclusion
relation. By imposing different conditions on the family of subsets, we can define several types of
granular structures. A number of studies, including rough set analysis, formal concept analysis and knowledge spaces, adopt specific models of granular structures. The proposed framework therefore provides a common ground for unifying these studies. The notion of approximations is examined based on granular structures.







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