IFIP International Conference on Artificial Intelligence in Theory and Practice

IFIP AI 2006: Artificial Intelligence in Theory and Practice pp 169-178

Comparison of SVM and Some Older Classification Algorithms in Text Classification Tasks

  • Fabrice Colas
  • Pavel Brazdil
Conference paper

DOI: 10.1007/978-0-387-34747-9_18

Volume 217 of the book series IFIP International Federation for Information Processing (IFIPAICT)
Cite this paper as:
Colas F., Brazdil P. (2006) Comparison of SVM and Some Older Classification Algorithms in Text Classification Tasks. In: Bramer M. (eds) Artificial Intelligence in Theory and Practice. IFIP AI 2006. IFIP International Federation for Information Processing, vol 217. Springer, Boston, MA

Summary

Document classification has already been widely studied. In fact, some studies compared feature selection techniques or feature space transformation whereas some others compared the performance of different algorithms. Recently, following the rising interest towards the Support Vector Machine, various studies showed that SVM outperforms other classification algorithms. So should we just not bother about other classification algorithms and opt always for SVM ?

We have decided to investigate this issue and compared SVM to kNN and naive Bayes on binary classification tasks. An important issue is to compare optimized versions of these algorithms, which is what we have done. Our results show all the classifiers achieved comparable performance on most problems. One surprising result is that SVM was not a clear winner, despite quite good overall performance. If a suitable preprocessing is used with kNN, this algorithm continues to achieve very good results and scales up well with the number of documents, which is not the case for SVM. As for naive Bayes, it also achieved good performance.

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Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  • Fabrice Colas
    • 1
  • Pavel Brazdil
    • 2
  1. 1.LIACSLeiden UniversityThe Netherlands
  2. 2.LIACC-NIAADUniversity of PortoPortugal