Symbolic Representation of Neural Networks
Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
An interactive-graphic environment for automatic generation of decision trees
Decision Support Systems
Neural networks approach to early breast cancer detection
Journal of Systems Architecture: the EUROMICRO Journal - Special issue on artificial neural networks
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Clustering Algorithms
SSVM: A Smooth Support Vector Machine for Classification
Computational Optimization and Applications
Machine Learning
Maintaining Case-Based Reasoning Systems Using Fuzzy Decision Trees
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Ontology-based intelligent healthcare agent and its application to respiratory waveform recognition
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A TSK type fuzzy rule based system for stock price prediction
Expert Systems with Applications: An International Journal
Ontological fuzzy agent for electrocardiogram application
Expert Systems with Applications: An International Journal
Evolving and clustering fuzzy decision tree for financial time series data forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An interpretable fuzzy rule-based classification methodology for medical diagnosis
Artificial Intelligence in Medicine
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Understanding neural networks via rule extraction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A complete fuzzy discriminant analysis approach for face recognition
Applied Soft Computing
Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry
Expert Systems with Applications: An International Journal
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A Hybrid System Integrating a Wavelet and TSK Fuzzy Rules for Stock Price Forecasting
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Fuzzy Systems
Fuzzy ARTMAP and hybrid evolutionary programming for pattern classification
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Evolutionary neural networks for practical applications
A two-phase case-based distance approach for multiple-group classification problems
Computers and Industrial Engineering
Artificial Intelligence in Medicine
Cross-document structural relationship identification using supervised machine learning
Applied Soft Computing
Expert Systems with Applications: An International Journal
A threshold fuzzy entropy based feature selection for medical database classification
Computers in Biology and Medicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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In this research, a hybrid model is developed by integrating a case-based data clustering method and a fuzzy decision tree for medical data classification. Two datasets from UCI Machine Learning Repository, i.e., liver disorders dataset and Breast Cancer Wisconsin (Diagnosis), are employed for benchmark test. Initially a case-based clustering method is applied to preprocess the dataset thus a more homogeneous data within each cluster will be attainted. A fuzzy decision tree is then applied to the data in each cluster and genetic algorithms (GAs) are further applied to construct a decision-making system based on the selected features and diseases identified. Finally, a set of fuzzy decision rules is generated for each cluster. As a result, the FDT model can accurately react to the test data by the inductions derived from the case-based fuzzy decision tree. The average forecasting accuracy for breast cancer of CBFDT model is 98.4% and for liver disorders is 81.6%. The accuracy of the hybrid model is the highest among those models compared. The hybrid model can produce accurate but also comprehensible decision rules that could potentially help medical doctors to extract effective conclusions in medical diagnosis.