Abstract
Management of data imprecision and uncertainty has become increasingly important, especially in situation awareness and assessment applications where reliability of the decision-making process is critical (e.g., in military battlefields). These applications require the following: 1) an effective methodology for modeling data imperfections and 2) procedures for enabling knowledge discovery and quantifying and propagating partial or incomplete knowledge throughout the decision-making process. In this paper, using a Dempster-Shafer belief-theoretic relational database (DS-DB)that can conveniently represent a wider class of data imperfections, an association rule mining (ARM)-based classification algorithm possessing the desirable functionality is proposed. For this purpose, various ARM-related notions are revisited so that they could be applied in the presence of data imperfections. A data structure called belief itemset tree is used to efficiently extract frequent itemsets and generate association rules from the proposed DS-DB. This set of rules is used as the basis on which an unknown data record, whose attributes are represented via belief functions, is classified. These algorithms are validated on a simplified situation assessment scenario where sensor observations may have caused data imperfections in both attribute values and class labels.
Original language | English |
---|---|
Pages (from-to) | 1446-1459 |
Number of pages | 14 |
Journal | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics |
Volume | 37 |
Issue number | 6 |
DOIs | |
State | Published - Dec 1 2007 |
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Keywords
- Association rule mining (ARM)
- Classification
- Data imperfections
- Data mining
- Dempster-Shafer (DS) belief theory
- Situation assessment
- Uncertainty handling
ASJC Scopus subject areas
- Control and Systems Engineering
- Artificial Intelligence
- Human-Computer Interaction
Cite this
Rule mining and classification in a situation assessment application : A belief-theoretic approach for handling data imperfections. / Rohitha, K. K.; Hewawasam, G. K.; Premaratne, Kamal; Shyu, Mei-Ling.
In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 37, No. 6, 01.12.2007, p. 1446-1459.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Rule mining and classification in a situation assessment application
T2 - A belief-theoretic approach for handling data imperfections
AU - Rohitha, K. K.
AU - Hewawasam, G. K.
AU - Premaratne, Kamal
AU - Shyu, Mei-Ling
PY - 2007/12/1
Y1 - 2007/12/1
N2 - Management of data imprecision and uncertainty has become increasingly important, especially in situation awareness and assessment applications where reliability of the decision-making process is critical (e.g., in military battlefields). These applications require the following: 1) an effective methodology for modeling data imperfections and 2) procedures for enabling knowledge discovery and quantifying and propagating partial or incomplete knowledge throughout the decision-making process. In this paper, using a Dempster-Shafer belief-theoretic relational database (DS-DB)that can conveniently represent a wider class of data imperfections, an association rule mining (ARM)-based classification algorithm possessing the desirable functionality is proposed. For this purpose, various ARM-related notions are revisited so that they could be applied in the presence of data imperfections. A data structure called belief itemset tree is used to efficiently extract frequent itemsets and generate association rules from the proposed DS-DB. This set of rules is used as the basis on which an unknown data record, whose attributes are represented via belief functions, is classified. These algorithms are validated on a simplified situation assessment scenario where sensor observations may have caused data imperfections in both attribute values and class labels.
AB - Management of data imprecision and uncertainty has become increasingly important, especially in situation awareness and assessment applications where reliability of the decision-making process is critical (e.g., in military battlefields). These applications require the following: 1) an effective methodology for modeling data imperfections and 2) procedures for enabling knowledge discovery and quantifying and propagating partial or incomplete knowledge throughout the decision-making process. In this paper, using a Dempster-Shafer belief-theoretic relational database (DS-DB)that can conveniently represent a wider class of data imperfections, an association rule mining (ARM)-based classification algorithm possessing the desirable functionality is proposed. For this purpose, various ARM-related notions are revisited so that they could be applied in the presence of data imperfections. A data structure called belief itemset tree is used to efficiently extract frequent itemsets and generate association rules from the proposed DS-DB. This set of rules is used as the basis on which an unknown data record, whose attributes are represented via belief functions, is classified. These algorithms are validated on a simplified situation assessment scenario where sensor observations may have caused data imperfections in both attribute values and class labels.
KW - Association rule mining (ARM)
KW - Classification
KW - Data imperfections
KW - Data mining
KW - Dempster-Shafer (DS) belief theory
KW - Situation assessment
KW - Uncertainty handling
UR - http://www.scopus.com/inward/record.url?scp=36749083967&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=36749083967&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2007.903536
DO - 10.1109/TSMCB.2007.903536
M3 - Article
C2 - 18179065
AN - SCOPUS:36749083967
VL - 37
SP - 1446
EP - 1459
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SN - 1083-4419
IS - 6
ER -