Discrete mathematics (3rd ed.)
Discrete mathematics (3rd ed.)
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Communications of the ACM
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
A multiclass neural network classifier with fuzzy teaching inputs
Fuzzy Sets and Systems
A neural network classifier with disjunctive fuzzy information
Neural Networks
A fuzzy inductive learning strategy for modular rules
Fuzzy Sets and Systems
Machine Learning
Text categorization using weight adjusted k-nearest neighbor classification (information retrieval)
Text categorization using weight adjusted k-nearest neighbor classification (information retrieval)
Automatically integrating multiple rule sets in adistributed-knowledge environment
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Integrating fuzzy knowledge by genetic algorithms
IEEE Transactions on Evolutionary Computation
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning pattern classification-a survey
IEEE Transactions on Information Theory
Neural-network feature selector
IEEE Transactions on Neural Networks
Classifier design with feature selection and feature extraction using layered genetic programming
Expert Systems with Applications: An International Journal
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Classification is an important research topic in knowledge discovery and data mining. Many different classifiers have been motivated and developed of late years. In this paper, we propose an effective scheme for learning multicategory classifiers based on genetic programming. For a k-class classification problem, a training strategy called adaptive incremental learning strategy and a new fitness function are used to generate k discriminant functions. We urge the discriminant functions to map the domains of training data into a specified interval, and thus data will be assigned into one of the classes by the values of functions. Furthermore, a Z-value measure is developed for resolving the conflicts. The experimental results show that the proposed GP-based classification learning approach is effective and performs a high accuracy of classification.