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 Naieve-Bayesian classifiers.
展开▼
