Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Mapping a manifold of perceptual observations
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A Decision Tree-Based Approach to Mining the Rules of Concept Drift
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
Analyzing Behavior of Objective Rule Evaluation Indices Based on a Correlation Coefficient
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
Mining decision rules on data streams in the presence of concept drifts
Expert Systems with Applications: An International Journal
Online updating belief rule based system for pipeline leak detection under expert intervention
Expert Systems with Applications: An International Journal
A sequential learning algorithm for online constructing belief-rule-based systems
Expert Systems with Applications: An International Journal
A method of discovering important rules using rules as attributes
International Journal of Intelligent Systems - Granular Computing: Models and Applications
Expert Systems with Applications: An International Journal
Classic Works of the Dempster-Shafer Theory of Belief Functions
Classic Works of the Dempster-Shafer Theory of Belief Functions
Inference analysis and adaptive training for belief rule based systems
Expert Systems with Applications: An International Journal
Computers and Operations Research
Rule-based agents, compliance, and intention reconsideration in defeasible logic
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
Optimization algorithm for learning consistent belief rule-base from examples
Journal of Global Optimization
Visualization of similarities and dissimilarities in rules using multidimensional scaling
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Belief rule-base inference methodology using the evidential reasoning Approach-RIMER
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optimization Models for Training Belief-Rule-Based Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Information Sciences: an International Journal
Online Updating Belief-Rule-Base Using the RIMER Approach
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Group consensus based on evidential reasoning approach using interval-valued belief structures
Knowledge-Based Systems
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The Belief Rule Base (BRB) is an expert system which can handle both qualitative and quantitative information. One of the applications of the BRB is the Rule-base Inference Methodology using the Evidential Reasoning approach (RIMER). Using the BRB, RIMER can handle different types of information under uncertainty. However, there is a combinatorial explosion problem when there are too many attributes and/or too many alternatives for each attribute in the BRB. Most current approaches are designed to reduce the number of the alternatives for each attribute, where the rules are derived from physical systems and redundant in numbers. However, these approaches are not applicable when the rules are given by experts and the BRB should not be oversized. A structure learning approach is proposed using Grey Target (GT), Multidimensional Scaling (MDS), Isomap and Principle Component Analysis (PCA) respectively, named as GT-RIMER, MDS-RIMER, Isomap-RIMER and PCA-RIMER. A case is studied to evaluate the overall capability of an Armored System of Systems. The efficiency of the proposed approach is validated by the case study results: the BRB is downsized using any of the four techniques, and PCA-RIMER has shown excellent performance. Furthermore, the robustness of PCA-RIMER is further verified under different conditions with varied number of attributes.