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 language | English |
|---|---|
| Title of host publication | Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007 |
| Pages | 598-603 |
| Number of pages | 6 |
| State | Published - Dec 28 2007 |
| Event | 20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007 - Key West, FL, United States Duration: May 7 2007 → May 9 2007 |
Other
| Other | 20th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007 |
|---|---|
| Country | United States |
| City | Key West, FL |
| Period | 5/7/07 → 5/9/07 |
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ASJC Scopus subject areas
- Artificial Intelligence
- Software
Cite this
Instance-based classifiers dealing with ambiguous attributes and class labels. / Holland, Hans; Kubat, Miroslav; Žižka, Jan.
Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007. 2007. p. 598-603.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Instance-based classifiers dealing with ambiguous attributes and class labels
AU - Holland, Hans
AU - Kubat, Miroslav
AU - Žižka, Jan
PY - 2007/12/28
Y1 - 2007/12/28
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:37349023594
SN - 1577353196
SN - 9781577353195
SP - 598
EP - 603
BT - Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference, FLAIRS 2007
ER -