Machine Learning and Data Mining; Methods and Applications
Machine Learning and Data Mining; Methods and Applications
Classification Rule Discovery with Ant Colony Optimization
IAT '03 Proceedings of the IEEE/WIC International Conference on Intelligent Agent Technology
Ant Colony Optimization
Ant colony optimization theory: a survey
Theoretical Computer Science
Intrusion detection using a fuzzy genetics-based learning algorithm
Journal of Network and Computer Applications - Special issue: Network and information security: A computational intelligence approach
ESTDD: Expert system for thyroid diseases diagnosis
Expert Systems with Applications: An International Journal
Induction of Fuzzy Classification Systems Using Evolutionary ACO-Based Algorithms
AMS '07 Proceedings of the First Asia International Conference on Modelling & Simulation
Data mining with a simulated annealing based fuzzy classification system
Pattern Recognition
Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
A comparative study on thyroid disease diagnosis using neural networks
Expert Systems with Applications: An International Journal
Rule extraction from trained adaptive neural networks using artificial immune systems
Expert Systems with Applications: An International Journal
Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications
Learning and Intelligent Optimization
Applying fuzzy neural network to estimate software development effort
Applied Intelligence
Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer
Journal of Medical Systems
Expert Systems with Applications: An International Journal
Support Vector Machine incorporated with feature discrimination
Expert Systems with Applications: An International Journal
FCACO: fuzzy classification rules mining algorithm with ant colony optimization
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Selecting fuzzy if-then rules for classification problems using genetic algorithms
IEEE Transactions on Fuzzy Systems
On the use and usefulness of fuzzy sets in medical AI
Artificial Intelligence in Medicine
Transductive cost-sensitive lung cancer image classification
Applied Intelligence
Skin cancer extraction with optimum fuzzy thresholding technique
Applied Intelligence
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The advantage of efficient searches belonging to ant-miner over several other approaches leads to prominent achievements on rules mining. Fuzzy ant-miner, an extension of the ant-miner provides a fuzzy mining framework for the automatic extraction of fuzzy rules from labeled numerical data. However, it is easily trapped in local optimal, especially when it applies to medical cases, where real world accuracy is elusive; and the interpretation and integration of medical knowledge is necessary. In order to relieve such a local optimal difficulty, this paper proposes OMFAM which applies simulated annealing to optimize fuzzy set parameters associated with a modified fuzzy ant-miner (MFAM). MFAM employs attributes and training case weighting. The proposed method, OMFAM was experimented with six critical medical cases for developing efficient medical diagnosis systems. The performance measurement relates to accuracy as well as interpretability of the mined rules. The performance of the OMFAM is compared with such references as MFAM, fuzzy ant-miner (FAM), and other classification methods. At last, it indicates the superiority of the OMFAM algorithm over the others.