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Two-stage learning for multi-class classification using genetic programming
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DOI：10.1016/j.neucom.2012.01.048.png)
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This paper introduces a two-stage strategy for multi-class classification problems. The proposed technique is an advancement of tradition binary decomposition method. In the first stage, the classifiers are trained for each class versus the remaining classes. A modified fitness value is used to select good discriminators for the imbalanced data. In the second stage, the classifiers are integrated and treated as a single chromosome that can classify any of the classes from the dataset. A population of such classifier-chromosomes is created from good classifiers (for individual classes) of the first phase. This population is evolved further, with a fitness that combines accuracy and conflicts. The proposed method encourages the classifier combination with good discrimination among all classes and less conflicts. The two-stage learning has been tested on several benchmark datasets and results are found encouraging. (C) 2012 Elsevier B.V. All rights reserved.
Keyword:
Classification
Genetic programming
Classifier
Expression
Rule
Algorithm
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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