Journal ArticleDOI
An information filtering model on the Web and its application in JobAgent
TLDR
A model for information filtering on the Web with rough set decision theory is proposed, and it shows that the rough set based model can provide an efficient approach to solve the information overload problem.Abstract:
Machine-learning techniques play the important roles for information filtering. The main objective of machine-learning is to obtain users' profiles. To decrease the burden of on-line learning, it is important to seek suitable structures to represent user information needs. This paper proposes a model for information filtering on the Web. The user information need is described into two levels in this model: profiles on category level, and Boolean queries on document level. To efficiently estimate the relevance between the user information need and documents, the user information need is treated as a rough set on the space of documents. The rough set decision theory is used to classify the new documents according to the user information need. In return for this, the new documents are divided into three parts: positive region, boundary region, and negative region. An experimental system JobAgent is also presented to verify this model, and it shows that the rough set based model can provide an efficient approach to solve the information overload problem.read more
Citations
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Journal ArticleDOI
Three-way decisions with probabilistic rough sets
TL;DR: This paper provides an analysis of three-way decision rules in the classical rough set model and the decision-theoretic rough set models, enriched by ideas from Bayesian decision theory and hypothesis testing in statistics.
Journal ArticleDOI
The superiority of three-way decisions in probabilistic rough set models
TL;DR: It is shown that, under certain conditions when considering the costs of different types of miss-classifications, probabilistic three-way decisions are superior to the other two.
Journal ArticleDOI
Probabilistic rough set approximations
TL;DR: Based on rough membership functions and rough inclusion functions, the Bayesian decision-theoretic analysis is adopted to provide a systematic method for determining the precision parameters by using more familiar notions of costs and risks.
Book ChapterDOI
Decision-theoretic rough set models
TL;DR: It is shown that the decision-theoretic models need to consider additional issues in probabilistic rough set models.
Journal ArticleDOI
Effective Pattern Discovery for Text Mining
TL;DR: This paper presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving, to improve the effectiveness of using and updating discovered patterns for finding relevant and interesting information.
References
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Book
Introduction to Modern Information Retrieval
Gerard Salton,Michael J. McGill +1 more
TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.
Book
Modern Information Retrieval
TL;DR: In this article, the authors present a rigorous and complete textbook for a first course on information retrieval from the computer science (as opposed to a user-centred) perspective, which provides an up-to-date student oriented treatment of the subject.
Book
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Journal ArticleDOI
Bayesian Reinforcement Learning in Factored POMDPs
TL;DR: This paper provides an overview of research and development activities in the field of autonomous agents and multi-agent systems and aims to identify key concepts and applications, and to indicate how they relate to one-another.
Journal ArticleDOI
Information filtering and information retrieval: two sides of the same coin?
TL;DR: Models of information retrieval and filtering, and lessons for filtering from retrieval research are presented; users see only the data that is extracted.