A decision theoretic approach to hierarchical classifier design
Pattern Recognition
Structure identification of fuzzy model
Fuzzy Sets and Systems
A Method for Attribute Selection in Inductive Learning Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Learning as a Function of Concept Character
Machine Learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
The Utility of Knowledge in Inductive Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Induction of fuzzy decision trees
Fuzzy Sets and Systems
Automatic induction of fuzzy decision trees and its application to power system security assessment
Fuzzy Sets and Systems - Special issue on applications of fuzzy theory in electronic power systems
Globally Optimal Fuzzy Decision Trees for Classification and Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Machine Learning
Machine Learning
Fuzzy classification trees for data analysis
Fuzzy Sets and Systems
On growing better decision trees from data
On growing better decision trees from data
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Medical data mining by fuzzy modeling with selected features
Artificial Intelligence in Medicine
Recent Literature Collected by Didier DUBOIS, Henri PRADE and Salvatore SESSA
Fuzzy Sets and Systems
DBSMOTE: Density-Based Synthetic Minority Over-sampling TEchnique
Applied Intelligence
Transductive cost-sensitive lung cancer image classification
Applied Intelligence
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To deal with highly uncertain and noisy data, for example, biochemical laboratory examinations, a classifier is required to be able to classify an instance into all possible classes and each class is associated with a degree which shows how possible an instance is in that class. According to these degrees, we can discriminate the more possible classes from the less possible classes. The classifier or an expert can pick the most possible one to be the instance class. However, if their discrimination is not distinguishable, it is better that the classifier should not make any prediction, especially when there is incomplete or inadequate data. A fuzzy classifier is proposed to classify the data with noise and uncertainties. Instead of determining a single class for a given instance, fuzzy classification predicts the degree of possibility for every class.Adenomatous polyps are widely accepted to be precancerous lesions and will degenerate into cancers ultimately. Therefore, it is important to generate a predictive method that can identify the patients who have obtained polyps and remove the lesions of them. Considering the uncertainties and noise in the biochemical laboratory examination data, fuzzy classification trees, which integrate decision tree techniques and fuzzy classifications, provide the efficient way to classify the data in order to generate the model for polyp screening.