Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Using Genetic Algorithms for Concept Learning
Machine Learning - Special issue on genetic algorithms
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Evolutionary approaches to fuzzy modelling for classification
The Knowledge Engineering Review
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Using an evolutionary approach for evolving classifiers can simplify the classification task. It requires no domain knowledge of the data to be classified nor the requirement to decide which attribute to select for partitioning. Our method, called the Genetic Evolved Classifier (GEC), uses a simple structured genetic algorithm to evolve classifiers. Besides being able to evolve rules to classify data in to multi-classes, it also provides a simple way to partition continuous data into discrete intervals, i.e., transform all types of attribute values into enumerable types. Experiment results shows that our approach produces promising results and is comparable to methods like C4.5, Fuzzy-ID3 (F-ID3), and probabilistic models such as modified Na茂ve-Bayesian classifiers.