TY - GEN

T1 - Constraint-based attribute reduction in rough set analysis

AU - Fan, Tuan Fang

AU - Liau, Churn Jung

AU - Liu, Duen-Ren

PY - 2010/12/1

Y1 - 2010/12/1

N2 - Attribute reduction is very important in rough set-based data analysis (RSDA) because it can be used to simplify the induced decision rules without reducing the classification accuracy. The notion of reduct plays a key role in rough set-based attribute reduction. In rough set theory, a reduct is generally defined as a minimal subset of attributes that can classify the same domain of objects as unambiguously as the original set of attributes. Nevertheless, from a relational perspective, RSDA relies on a kind of dependency constraint. That is, the relationship between the class labels of a pair of objects depends on the componentwise comparison of their condition attributes. The larger the number of condition attributes compared, the greater the probability that the constraint will hold. Thus, elimination of condition attributes may cause more object pairs to violate the constraint. Based on this observation, a reduct can be defined alternatively as a minimal subset of attributes that does not increase the number of objects violating the constraint. While the alternative definition coincides with the original one in ordinary RSDA, it is more easily generalized to cases of fuzzy RSDA and relational data analysis.

AB - Attribute reduction is very important in rough set-based data analysis (RSDA) because it can be used to simplify the induced decision rules without reducing the classification accuracy. The notion of reduct plays a key role in rough set-based attribute reduction. In rough set theory, a reduct is generally defined as a minimal subset of attributes that can classify the same domain of objects as unambiguously as the original set of attributes. Nevertheless, from a relational perspective, RSDA relies on a kind of dependency constraint. That is, the relationship between the class labels of a pair of objects depends on the componentwise comparison of their condition attributes. The larger the number of condition attributes compared, the greater the probability that the constraint will hold. Thus, elimination of condition attributes may cause more object pairs to violate the constraint. Based on this observation, a reduct can be defined alternatively as a minimal subset of attributes that does not increase the number of objects violating the constraint. While the alternative definition coincides with the original one in ordinary RSDA, it is more easily generalized to cases of fuzzy RSDA and relational data analysis.

KW - Classical rough set

KW - Core

KW - Dominance-based rough set

KW - Fuzzy rough set

KW - Reduct

KW - Relational information system

UR - http://www.scopus.com/inward/record.url?scp=78751488446&partnerID=8YFLogxK

U2 - 10.1109/ICSMC.2010.5642233

DO - 10.1109/ICSMC.2010.5642233

M3 - Conference contribution

AN - SCOPUS:78751488446

SN - 9781424465880

T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics

SP - 3971

EP - 3976

BT - 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010

T2 - 2010 IEEE International Conference on Systems, Man and Cybernetics, SMC 2010

Y2 - 10 October 2010 through 13 October 2010

ER -