MC3: A multi-class consensus classification framework

Tanmoy Chakraborty*, Des Chandhok, V. S. Subrahmanian

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

In this paper, we propose MC3, an ensemble framework for multi-class classification. MC3 is built on “consensus learning”, a novel learning paradigm where each individual base classifier keeps on improving its classification by exploiting the outcomes obtained from other classifiers until a consensus is reached. Based on this idea, we propose two algorithms, MC3-R and MC3-S that make different trade-offs between quality and runtime. We conduct rigorous experiments comparing MC3-R and MC3-S with 12 baseline classifiers on 13 different datasets. Our algorithms perform as well or better than the best baseline classifier, achieving on average, a 5.56% performance improvement. Moreover, unlike existing baseline algorithms, our algorithms also improve the performance of individual base classifiers up to 10%. (The code is available at https://github.com/MC3-code.).

Original languageEnglish (US)
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
EditorsKyuseok Shim, Jae-Gil Lee, Longbing Cao, Xuemin Lin, Jinho Kim, Yang-Sae Moon
PublisherSpringer Verlag
Pages343-355
Number of pages13
ISBN (Print)9783319574530
DOIs
StatePublished - 2017
Externally publishedYes
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
Duration: May 23 2017May 26 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10234 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
Country/TerritoryKorea, Republic of
CityJeju
Period5/23/175/26/17

Keywords

  • Consensus
  • Ensemble learning
  • Multi-class classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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