Affiliation:
1. Qingdao Vocational and Technical College of Hotel Management, Qingdao 266100, China
2. College of Computer Science and Technology, Ocean University of China, Qingdao 266404, China
3. College of Information Science and Technology, Shihezi University, Shihezi 832003, China
Abstract
Ensemble learning, online learning and deep learning are very effective and versatile in a wide spectrum of problem domains, such as feature extraction, multi-class classification and retrieval. In this paper, combining the ideas of ensemble learning, online learning and deep learning, we propose a novel deep learning method called deep error-correcting output codes (DeepECOCs). DeepECOCs are composed of multiple layers of the ECOC module, which combines several incremental support vector machines (incremental SVMs) as base classifiers. In this novel deep architecture, each ECOC module can be considered as two successive layers of the network, while the incremental SVMs can be viewed as weighted links between two successive layers. In the pre-training procedure, supervisory information, i.e., class labels, can be used during the network initialization. The incremental SVMs lead this procedure to be very efficient, especially for large-scale applications. We have conducted extensive experiments to compare DeepECOCs with traditional ECOC, feature learning and deep learning algorithms. The results demonstrate that DeepECOCs perform, not only better than existing ECOC and feature learning algorithms, but also related to deep learning ones in most cases.
Funder
National Key Research and Development Program of China
HY Project
Natural Science Foundation of Shandong Province
Science and Technology Program of Qingdao
Project of Associative Training of Ocean University of China
Subject
Computational Mathematics,Computational Theory and Mathematics,Numerical Analysis,Theoretical Computer Science
Reference53 articles.
1. Solving multiclass learning problems via error-correcting output codes;Dietterich;J. Artif. Intell. Res.,1995
2. Random Forests;Breiman;Mach. Learn.,2001
3. A Brief Introduction to Boosting;Schapire;IJCAI,2010
4. Kumar, A., Kaur, A., Singh, P., Driss, M., and Boulila, W. (2023). Efficient Multiclass Classification Using Feature Selection in High-Dimensional Datasets. Electronics, 12.
5. Saeed, M.M., Saeed, R.A., Abdelhaq, M., Alsaqour, R., Hasan, M.K., and Mokhtar, R.A. (2023). Anomaly Detection in 6G Networks Using Machine Learning Methods. Electronics, 12.
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