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- Title
Dynamically building diversified classifier pruning ensembles via canonical correlation analysis.
- Authors
Jiang, Zhong-Qiu; Shen, Xiang-Jun; Gou, Jian-Ping; Wang, Liangjun; Zha, Zheng-Jun
- Abstract
Empirical 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 method of dynamically building diversified sparse ensembles. We first apply a technique known as the canonical correlation to model the relationship between the input data variables and output base classifiers. The canonical (projected) output classifiers and input training data variables are encoded globally through a multi-linear projection of CCA, to decrease the impacts of noisy input data and incorrect classifiers to a minimum degree in such a global view. Secondly, based on the projection, a sparse regression method is used to prune representative classifiers by combining classifier diversity measurement. Based on the above methods, we evaluate the proposed approach by several datasets, such as UCI and handwritten digit recognition. Experimental results of the study show that, the proposed approach achieves better accuracy as compared to other ensemble methods such as QFWEC, Simple Vote Rule, Random Forest, Drep and Adaboost.
- Subjects
LEARNING; CANONICAL correlation (Statistics); STATISTICAL correlation; RANDOM forest algorithms; RESEARCH methodology
- Publication
Multimedia Tools & Applications, 2019, Vol 78, Issue 1, p271
- ISSN
1380-7501
- Publication type
Article
- DOI
10.1007/s11042-018-5718-x