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Chapter
Vagueness and Uncertainty: An F-Rough Set Perspective
F-rough sets are the first dynamical rough set model for a family of information systems (decision systems). This chapter investigates vagueness and uncertainty from the viewpoints of F-rough sets. Some indexe...
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Chapter and Conference Paper
Parallel Reducts: A Hashing Approach
A hashing approach in parallel reducts is clearly presented in this paper. With the help of this new approach, time-consuming comparison operations reduce significantly, therefore, matrix of attribute signific...
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Chapter and Conference Paper
Subjectivity and Objectivity of Trust
Trust plays an important role in the fields of Distributed Artificial Intelligence (DAI) and Multi-agent Systems (MAS), which provides a more effective way to reduce complexity in condition of increasing socia...
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Chapter and Conference Paper
An Improved Iterated Local Search Algorithm for the Permutation Flowshop Problem with Total Flowtime
Iterated local search (ILS) algorithm is a powerful metaheuristic for the permutation flowshop problem with total flowtime objective. ILS is based on a local search procedure and the procedure needs to be rest...
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Chapter and Conference Paper
GTrust: A Distributed Trust Model in Multi-Agent Systems Based on Grey System Theory
Trust model is a key problem in Multi-Agent System. In this paper, a distributed trust model called GTrust is constructed to help agents choose partners in their interactions. The grey system theory, which is ...
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Chapter and Conference Paper
Study on Iterated Local Search Algorithm for Permutation Flowshop Problem with Total Flowtime Objective
Iterated Local Search (ILS) is a simple and efficient algorithm, which has been used to solve many combinatorial optimization problems. However, the effect of different search sequences in the local search pro...
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Chapter and Conference Paper
Community-Based Relational Markov Networks in Complex Networks
Relational Markov networks (RMNs) are a joint probabilistic model for an entire collection of related entities. The model is able to mine relational data effectively by integrating information from content att...
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Chapter and Conference Paper
Unsupervised Feature Weighting Based on Local Feature Relatedness
Feature weighting plays an important role in text clustering. Traditional feature weighting is determined by the syntactic relationship between feature and document (e.g. TF-IDF). In this paper, a semantically...
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Chapter and Conference Paper
A Community-Based Pseudolikelihood Approach for Relationship Labeling in Social Networks
A social network consists of people (or other social entities) connected by a set of social relationships. Awareness of the relationship types is very helpful for us to understand the structure and the charact...
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Article
TOPSIL-Miner: an efficient algorithm for mining top-K significant itemsets over data streams
Frequent itemset mining over data streams becomes a hot topic in data mining and knowledge discovery in recent years, and has been applied to different areas. However, the setting of a minimum support threshol...
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Chapter and Conference Paper
Semantics-Based Representation Model for Multi-layer Text Classification
Text categorization is one of the most common themes in data mining and machine learning fields. Unlike structured data, unstructured text data is more complicated to be analyzed because it contains too much i...
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Article
Bring QoS to P2P-based semantic service discovery for the Universal Network
Services in the next generation Internet, Universal Network, is distinct from that in the current network. The reason is that the former has QoS (quality of sevice) grading. In the universal network, different...
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Chapter and Conference Paper
An Uncertainty-Based Belief Selection Method for POMDP Value Iteration
Partially Observable Markov Decision Process (POMDP) provides a probabilistic model for decision making under uncertainty. Point-based value iteration algorithms are effective approximate algorithms to solve P...
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Chapter and Conference Paper
A Selective Classifier for Incomplete Data
Classifiers based on feature selection (selective classifiers) are a kind of algorithms that can effectively improve the accuracy and efficiency of classification by deleting irrelevant or redundant attributes...
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Chapter
Research on Rough Set Theory and Applications in China
This article gives a capsule view of research on rough set theory and applications ongoing at universities and laboratories in China. Included in this capsule view of rough set research is a brief description ...
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Chapter and Conference Paper
A Discriminative Learning Method of TAN Classifier
TAN (Tree-augmented Naïve Bayes) classifier makes a compromise between the model complexity and classification rate, the study of which has now become a hot research issue. In this paper, we propose a discrimi...
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Chapter and Conference Paper
Semantic Service Discovery with QoS Measurement in Universal Network
The service of Universal Network is different from that of current network, because the former has QoS (Quality of Service) grading. Therefore, service discovery of Universal Network is quite distinct from tha...
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Chapter and Conference Paper
Synthesis of Concepts Based on Rough Set Theory
Rough set theory usually deals with how a concept is represented with some granules. In this paper we extend rough set theory, and use it to deal with how a series of concepts are represented with a granule. W...
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Chapter and Conference Paper
A Summary Structure of Data Cube Preserving Semantics
The semantic relations among cells in data cube are more important for efficient query and OLAP. Normally the size of a data cube is very huge and relations among cells are very complicated so the semantic data c...
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Chapter and Conference Paper
Granulation Based Approximate Ontologies Capture
Ontologies are of vital importance to the successful realization of semantic Web. Currently, the existing concepts in ontologies are not approximate but clear. However, in real application domains many concept...
