Abstract
This chapter introduces a belief theoretic method for classification from databases having class label ambiguities. It uses a set of association rules extracted from such a database. It is assumed that a training data set with an adequate number of pre-classified instances, where each instance is assigned with an integer class label, is available. We use a modified association rule mining (ARM) technique to extract the interesting rules from the training data set and use a belief theoretic classifier based on the extracted rules to classify the incoming feature vectors. The ambiguity modelling capability of belief theory enables our classifier to perform better in the presence of class label ambiguities. It can also address the issue of the training data set being unbalanced or highly skewed by ensuring that an approximately equal number of rules are generated for each class. All these capabilities make our classifier ideally suited for those applications where (1) different experts may have conflicting opinions about the class label to be assigned to a specific training data instance; and (2) the majority of the training data instances are likely to represent a few classes giving rise to highly skewed databases. Therefore, the proposed classifier would be extremely useful in security monitoring and threat classification environments where conflicting expert opinions about the threat level are common and only a few training data instances would be considered to pose a heightened threat level. Several experiments are conducted to evaluate our proposed classifier. These experiments use several databases from the UCI data repository and data sets collected from the airport terminal simulation platform developed at the Distributed Decision Environments (DDE) Laboratory at the Department of Electrical and Computer Engineering, University of Miami. The experimental results show that, while the proposed classifier's performance is comparable to some existing classifiers when the databases have no class label ambiguities, it provides superior classification accuracy and better efficiency when class label ambiguities are present.
| Original language | English |
|---|---|
| Title of host publication | Studies in Computational Intelligence |
| Pages | 539-562 |
| Number of pages | 24 |
| Volume | 118 |
| DOIs | |
| State | Published - Sep 15 2008 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 118 |
| ISSN (Print) | 1860949X |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
Using Association rules for classification from databases having class label ambiguities : A belief theoretic method. / Subasingha, S. P.; Zhang, J.; Premaratne, Kamal; Shyu, Mei-Ling; Kubat, Miroslav; Hewawasam, K. K R G K.
Studies in Computational Intelligence. Vol. 118 2008. p. 539-562 (Studies in Computational Intelligence; Vol. 118).Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Using Association rules for classification from databases having class label ambiguities
T2 - A belief theoretic method
AU - Subasingha, S. P.
AU - Zhang, J.
AU - Premaratne, Kamal
AU - Shyu, Mei-Ling
AU - Kubat, Miroslav
AU - Hewawasam, K. K R G K
PY - 2008/9/15
Y1 - 2008/9/15
N2 - This chapter introduces a belief theoretic method for classification from databases having class label ambiguities. It uses a set of association rules extracted from such a database. It is assumed that a training data set with an adequate number of pre-classified instances, where each instance is assigned with an integer class label, is available. We use a modified association rule mining (ARM) technique to extract the interesting rules from the training data set and use a belief theoretic classifier based on the extracted rules to classify the incoming feature vectors. The ambiguity modelling capability of belief theory enables our classifier to perform better in the presence of class label ambiguities. It can also address the issue of the training data set being unbalanced or highly skewed by ensuring that an approximately equal number of rules are generated for each class. All these capabilities make our classifier ideally suited for those applications where (1) different experts may have conflicting opinions about the class label to be assigned to a specific training data instance; and (2) the majority of the training data instances are likely to represent a few classes giving rise to highly skewed databases. Therefore, the proposed classifier would be extremely useful in security monitoring and threat classification environments where conflicting expert opinions about the threat level are common and only a few training data instances would be considered to pose a heightened threat level. Several experiments are conducted to evaluate our proposed classifier. These experiments use several databases from the UCI data repository and data sets collected from the airport terminal simulation platform developed at the Distributed Decision Environments (DDE) Laboratory at the Department of Electrical and Computer Engineering, University of Miami. The experimental results show that, while the proposed classifier's performance is comparable to some existing classifiers when the databases have no class label ambiguities, it provides superior classification accuracy and better efficiency when class label ambiguities are present.
AB - This chapter introduces a belief theoretic method for classification from databases having class label ambiguities. It uses a set of association rules extracted from such a database. It is assumed that a training data set with an adequate number of pre-classified instances, where each instance is assigned with an integer class label, is available. We use a modified association rule mining (ARM) technique to extract the interesting rules from the training data set and use a belief theoretic classifier based on the extracted rules to classify the incoming feature vectors. The ambiguity modelling capability of belief theory enables our classifier to perform better in the presence of class label ambiguities. It can also address the issue of the training data set being unbalanced or highly skewed by ensuring that an approximately equal number of rules are generated for each class. All these capabilities make our classifier ideally suited for those applications where (1) different experts may have conflicting opinions about the class label to be assigned to a specific training data instance; and (2) the majority of the training data instances are likely to represent a few classes giving rise to highly skewed databases. Therefore, the proposed classifier would be extremely useful in security monitoring and threat classification environments where conflicting expert opinions about the threat level are common and only a few training data instances would be considered to pose a heightened threat level. Several experiments are conducted to evaluate our proposed classifier. These experiments use several databases from the UCI data repository and data sets collected from the airport terminal simulation platform developed at the Distributed Decision Environments (DDE) Laboratory at the Department of Electrical and Computer Engineering, University of Miami. The experimental results show that, while the proposed classifier's performance is comparable to some existing classifiers when the databases have no class label ambiguities, it provides superior classification accuracy and better efficiency when class label ambiguities are present.
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U2 - 10.1007/978-3-540-78488-3_32
DO - 10.1007/978-3-540-78488-3_32
M3 - Chapter
SN - 9783540784876
VL - 118
T3 - Studies in Computational Intelligence
SP - 539
EP - 562
BT - Studies in Computational Intelligence
ER -