Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
On Objective Measures of Rule Surprisingness
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Mining customer product ratings for personalized marketing
Decision Support Systems - Special issue: Web data mining
An intelligent system for customer targeting: a data mining approach
Decision Support Systems
Particle swarm based Data Mining Algorithms for classification tasks
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
Developing the Method of Server Controlled Outcomes Management and Variance Analysis
Electronic Notes in Theoretical Computer Science (ENTCS)
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A new approach to systematization of the management of paper-based clinical pathways
Computer Methods and Programs in Biomedicine
Expert Systems with Applications: An International Journal
A GAs based approach for mining breast cancer pattern
Expert Systems with Applications: An International Journal
Optimal ensemble construction via meta-evolutionary ensembles
Expert Systems with Applications: An International Journal
Rule learning by searching on adapted nets
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Two sub-swarms particle swarm optimization algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
IEEE Transactions on Evolutionary Computation
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using Recommendation to Support Adaptive Clinical Pathways
Journal of Medical Systems
Expert Systems with Applications: An International Journal
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Although the clinical pathway (CP) predefines predictable standardized care process for a particular diagnosis or procedure, many variances may still unavoidably occur. Some key index parameters have strong relationship with variances handling measures of CP. In real world, these problems are highly nonlinear in nature so that it's hard to develop a comprehensive mathematic model. In this paper, a rule extraction approach based on combing hybrid genetic double multi-group cooperative particle swarm optimization algorithm (PSO) and discrete PSO algorithm (named HGDMCPSO/DPSO) is developed to discovery the previously unknown and potentially complicated nonlinear relationship between key parameters and variances handling measures of CP. Then these extracted rules can provide abnormal variances handling warning for medical professionals. Three numerical experiments on Iris of UCI data sets, Wisconsin breast cancer data sets and CP variances data sets of osteosarcoma preoperative chemotherapy are used to validate the proposed method. When compared with the previous researches, the proposed rule extraction algorithm can obtain the high prediction accuracy, less computing time, more stability and easily comprehended by users, thus it is an effective knowledge extraction tool for CP variances handling.