Deep N-ary Error Correcting Output Codes

• URL: http://arxiv.org/abs/2009.10465v4
• Date: Tue, 15 Dec 2020 02:33:15 GMT
• Title: Deep N-ary Error Correcting Output Codes
• Authors: Hao Zhang, Joey Tianyi Zhou, Tianying Wang, Ivor W. Tsang, Rick Siow Mong Goh
• Abstract summary: Data-independent ensemble methods like Error Correcting Output Codes (ECOC) attract increasing attention. N-ary ECOC decomposes the original multi-class classification problem into a series of independent simpler classification subproblems. We propose three different variants of parameter sharing architectures for deep N-ary ECOC.
• Score: 66.15481033522343
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensemble learning consistently improves the performance of multi-class classification through aggregating a series of base classifiers. To this end, data-independent ensemble methods like Error Correcting Output Codes (ECOC) attract increasing attention due to its easiness of implementation and parallelization. Specifically, traditional ECOCs and its general extension N-ary ECOC decompose the original multi-class classification problem into a series of independent simpler classification subproblems. Unfortunately, integrating ECOCs, especially N-ary ECOC with deep neural networks, termed as deep N-ary ECOC, is not straightforward and yet fully exploited in the literature, due to the high expense of training base learners. To facilitate the training of N-ary ECOC with deep learning base learners, we further propose three different variants of parameter sharing architectures for deep N-ary ECOC. To verify the generalization ability of deep N-ary ECOC, we conduct experiments by varying the backbone with different deep neural network architectures for both image and text classification tasks. Furthermore, extensive ablation studies on deep N-ary ECOC show its superior performance over other deep data-independent ensemble methods.

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