Experimental validation for N-ary error correcting output codes for ensemble learning of deep neural networks

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2 Citations (Scopus)

Abstract

N-ary error correcting output codes (ECOC) decompose a multi-class problem into simpler multi-class problems by splitting the classes into N subsets (meta-classes) to form an ensemble of N-class classifiers and combine them to make predictions. It is one of the most accurate ensemble learning methods for traditional classification tasks. Deep learning has gained increasing attention in recent years due to its successes on various tasks such as image classification and speech recognition. However, little is known about N-ary ECOC with deep neural networks (DNNs) as base learners, probably due to the long computation time. In this paper, we show by experiments that N-ary ECOC with DNNs as base learners generally exhibits superior performance compared with several state-of-the-art ensemble learning methods. Moreover, our work contributes to a more efficient setting of the two crucial hyperparameters of N-ary ECOC: the value of N and the number of base learners to train. We also explore valuable strategies for further improving the accuracy of N-ary ECOC.

Original languageEnglish
Pages (from-to)367-392
Number of pages26
JournalJournal of Intelligent Information Systems
Volume52
Issue number2
DOIs
Publication statusPublished - Apr 15 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

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