The Strength of Weak Learnability
Machine Learning
Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Utility Problem Analysed: A Case-Based Reasoning Perspective
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A genetic encoding approach for learning methods for combining classifiers
Expert Systems with Applications: An International Journal
A novel divide-and-merge classification for high dimensional datasets
Computational Biology and Chemistry
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Ensemble of classifiers is a learning paradigm where many classifiers are jointly used to solve a problem. Research has shown that ensemble is very effective for classification tasks. Diversity and accuracy are two basic requirements for the ensemble creation. In this paper, we propose an ensemble creation method based on GA wrapper feature selection. Preliminary experimental results on real-world data show that the proposed method is promising, especially when the number of training data is limited.