Instance-based classifiers dealing with ambiguous attributes and class labels

Hans Holland, Miroslav Kubat, Jan Žižka

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

1 Scopus citations

Abstract

Machine learning usually assumes that attribute values, as well as class labels, are either known precisely or not known at all. However, in our attempt to automate evaluation of intrusion detection systems, we have encountered ambiguous examples such that, for instance, an attribute's value in a given example is known to be a or b but definitely not c or d. Previous research usually either "disambiguated" the value by giving preference to a or b, or just replaced it with a "don't-know" symbol. Disliking both of these two approaches, we decided to explore the behavior of other ways to address the situation. To keep the work focused, we limited ourselves to nearest-neighbor classifiers. The paper describes a few techniques and reports relevant experiments. We also discuss certain ambiguity-related issues that deserve closer attention.

Original languageEnglish (US)
Title of host publicationProceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007
Pages598-603
Number of pages6
StatePublished - Dec 28 2007
Event20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007 - Key West, FL, United States
Duration: May 7 2007May 9 2007

Publication series

NameProceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007

Other

Other20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007
CountryUnited States
CityKey West, FL
Period5/7/075/9/07

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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  • Cite this

    Holland, H., Kubat, M., & Žižka, J. (2007). Instance-based classifiers dealing with ambiguous attributes and class labels. In Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007 (pp. 598-603). (Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007).