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.Comment: EAI MOBIMEDIA 202