Simultaneous meta-data and meta-classifier selection in multiple classifier system
| Autor: | Thi Minh Van Nguyen, Anh Vu Luong, Tien Thanh Nguyen, John McCall, Trong Sy Ha, Alan Wee-Chung Liew |
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| Rok vydání: | 2019 |
| Předmět: |
Training set
Computer science business.industry Ant colony optimization algorithms Feature selection Pattern recognition 0102 computer and information sciences 02 engineering and technology 01 natural sciences Multiple classifier Cross-validation Metadata ComputingMethodologies_PATTERNRECOGNITION 010201 computation theory & mathematics 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) |
| Zdroj: | GECCO |
| DOI: | 10.1145/3321707.3321770 |
| Popis: | In ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significantly depends on the choice of meta-classifier. Normally, the classifier selection method applied to an ensemble usually removes all the predictions of a classifier if this classifier is not selected in the final ensemble. Here we present an idea to only remove a subset of each classifier's prediction thereby introducing a simultaneous meta-data and meta-classifier selection method for ensemble systems. Our approach uses Cross Validation on the training set to generate meta-data as the predictions of base classifiers. We then use Ant Colony Optimization to search for the optimal subset of meta-data and meta-classifier for the data. By considering each column of meta-data, we construct the configuration including a subset of these columns and a meta-classifier. Specifically, the columns are selected according to their corresponding pheromones, and the meta-classifier is chosen at random. The classification accuracy of each configuration is computed based on Cross Validation on meta-data. Experiments on UCI datasets show the advantage of proposed method compared to several classifier and feature selection methods for ensemble systems. |
| Databáze: | OpenAIRE |
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