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Article

Improving multiclass text classification with error-correcting output coding and sub-class partitions

Authors:

Carl VogelAuthors Info & Claims

AI'10: Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence

Pages 4 - 15

https://doi.org/10.1007/978-3-642-13059-5_4

Published: 31 May 2010 Publication History

Abstract

Error-Correcting Output Coding (ECOC) is a general framework for multiclass text classification with a set of binary classifiers It can not only help a binary classifier solve multi-class classification problems, but also boost the performance of a multi-class classifier When building each individual binary classifier in ECOC, multiple classes are randomly grouped into two disjoint groups: positive and negative However, when training such a binary classifier, sub-class distribution within positive and negative classes is neglected Utilizing this information is expected to improve a binary classifier We thus design a simple binary classification strategy via multi-class categorization (2vM) to make use of sub-class partition information, which can lead to better performance over the traditional binary classification The proposed binary classification strategy is then applied to enhance ECOC Experiments on document categorization and question classification show its effectiveness.

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Ghani, R.: Using Error-Correcting Codes for Text Classification In: The Seventeenth International Conference on Machine Learning, ICML 2000 (2000)

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Mccallum, A., Nigam, K.: A Comparison of Event Models for Naive Bayes Text Classification In: The AAAI/ICML 1998 Workshop on Learning for Text Categorization (1998)

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Cardoso-Cachopo, A.: Improving Methods for Single-label Text Categorization PhD Thesis, Instituto Superior Técnico, Portugal (2007)

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Dietterich, T.G., Bakiri, G.: Error-correcting output codes: A general method for improving multiclass inductive learning programs In: The Ninth National Conference on Artificial Intelligence (AAAI 1991), pp 572-577 (1991)

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Berger, A.: Error-correcting output coding for text classification In: IJCAI 1999 Workshop on Machine Learning for Information Filtering (1999)

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Tan, S., Wu, G., Cheng, X.: Enhancing the Performance of Centroid Classifier by ECOC and Model Refinement In: Buntine, W., Grobelnik, M., Mladenic, D., Shawe-Taylor, J (eds.) ECML PKDD 2009, Part II LNCS, vol 5782, pp 458-472 Springer, Heidelberg (2009)

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Pujol, O., Radeva, P., Vitria, J.: Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes IEEE Transactions on Pattern Analysis and Machine Intelligence 28, 1007-1012 (2006)

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Cited By

Kuralenok IStarikova NKhvorov ASerdyuk JCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Construction of Efficient V-Gram Dictionary for Sequential Data AnalysisProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271789(1343-1352)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271789
Šajgalík MBarla MBieliková M(2014)Exploring Multidimensional Continuous Feature Space to Extract Relevant WordsStatistical Language and Speech Processing10.1007/978-3-319-11397-5_12(159-170)Online publication date: 14-Oct-2014
https://dl.acm.org/doi/10.1007/978-3-319-11397-5_12

Improving multiclass text classification with error-correcting output coding and sub-class partitions
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Information & Contributors

Information

Published In

cover image Guide Proceedings

AI'10: Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence

May 2010

423 pages

ISBN:3642130585

Editors:
Atefeh Farzindar
NLP Technologies Inc., 1255 University Street, Montreal, Quebec, Canada
,
Vlado Kešelj
Dalhousie University, Faculty of Computer Science, 6050 University Ave, Halifax, Nova Scotia, Quebec, Canada

Sponsors

MultiCorpora R&D: MultiCorpora R&D Inc.
University of Ottawa: University of Ottawa
Palomino System Innovations: Palomino System Innovations Inc.
CAIAC: Canadian Artificial Intelligence Association/Association pour l'intelligence artificielle au Canada
NLP Technologies Inc.: NLP Technologies Inc.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 31 May 2010

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Cited By

Kuralenok IStarikova NKhvorov ASerdyuk JCuzzocrea AAllan JPaton NSrivastava DAgrawal RBroder AZaki MCandan SLabrinidis ASchuster AWang H(2018)Construction of Efficient V-Gram Dictionary for Sequential Data AnalysisProceedings of the 27th ACM International Conference on Information and Knowledge Management10.1145/3269206.3271789(1343-1352)Online publication date: 17-Oct-2018
https://dl.acm.org/doi/10.1145/3269206.3271789
Šajgalík MBarla MBieliková M(2014)Exploring Multidimensional Continuous Feature Space to Extract Relevant WordsStatistical Language and Speech Processing10.1007/978-3-319-11397-5_12(159-170)Online publication date: 14-Oct-2014
https://dl.acm.org/doi/10.1007/978-3-319-11397-5_12

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