Building Locally Discriminative Classifier Ensemble Through Classifier Fusion Among Nearest Neighbors
| Autor: | Zheng-Jun Zha, Wen-Chao Zhang, He-Ping Song, Xiang-Jun Shen, Qian Zhu, Wei Cai, Ben-Bright B. Benuw |
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| Rok vydání: | 2016 |
| Předmět: |
business.industry
Computer science 0211 other engineering and technologies Pattern recognition Linear classifier 02 engineering and technology Bayes classifier Quadratic classifier Machine learning computer.software_genre Ensemble learning Random subspace method ComputingMethodologies_PATTERNRECOGNITION Margin classifier 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing AdaBoost Artificial intelligence business computer Cascading classifiers 021101 geological & geomatics engineering |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319488899 PCM (1) |
| Popis: | Many studies on ensemble learning that combines multiple classifiers have shown that, it is an effective technique to improve accuracy and stability of a single classifier. In this paper, we propose a novel discriminative classifier fusion method, which applies local classification results of classifiers among nearest neighbors to build a local classifier ensemble. From this dynamically selected process, discriminative classifiers are weighted heavily to build a locally discriminative ensemble. Experimental results on several UCI datasets have shown that, our proposed method achieves best classification performance among individual classifiers, majority voting and AdaBoost algorithms. |
| Databáze: | OpenAIRE |
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