A decision theoretic approach to hierarchical classifier design
Pattern Recognition
Large Tree Classifier with Heuristic Search and Global Training
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Structure identification of fuzzy model
Fuzzy Sets and Systems
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
Incremental Learning from Noisy Data
Machine Learning
On growing better decision trees from data
On growing better decision trees from data
A Modified K-means Algorithm for Sequence Clustering
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
Fuzzy decision trees: issues and methods
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
ID3-derived fuzzy rules and optimized defuzzification for handwritten numeral recognition
IEEE Transactions on Fuzzy Systems
Communication during learning in heterogeneous teams of learning agents
Intelligent Decision Technologies
Modelling fuzzy universal resource identifiers: A first approach
Mathematical and Computer Modelling: An International Journal
Towards new directions of data mining by evolutionary fuzzy rules and symbolic regression
Computers & Mathematics with Applications
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Overly generalized predictions are a serious problem in concept classification. In particular, the boundaries among classes are not always clearly defined. For example, there are usually uncertainties in diagnoses based on data from biochemical laboratory examinations. Such uncertainties make the prediction be more difficult than noise-free data. To avoid such problems, the idea of fuzzy classification is proposed. This paper presents the basic definition of fuzzy classification trees along with their construction algorithm. Fuzzy classification trees is a new model that integrates the fuzzy classifiers with decision trees, that can work well in classifying the data with noise. Instead of determining a single class for any given instance, fuzzy classification predicts the degree of possibility for every class.Some empirical results the dataset from UCI Repository are given for comparing FCT and C4.5. Generally speaking, FCT can obtain better results than C4.5.