TY - GEN
T1 - Lazy classification algorithms based on deterministic and inhibitory association rules
AU - Delimata, Pawel
AU - Moshkov, Mikhail Ju
AU - Skowron, Andrzej
AU - Suraj, Zbigniew
PY - 2009
Y1 - 2009
N2 - In this chapter, we consider the same classification problem as in Chap. 5: for a given decision table T and a new object v it is required to generate a value of the decision attribute on v using values of conditional attributes on v. To this end, we divide the decision table T into a number of information systems Si, i ∈ Dec (T), where Dec (T) is the set of values of the decision attribute in T. For i ∈ Dec (T), the information system Si contains only objects (rows) of T with the value of the decision attribute equal to i.
AB - In this chapter, we consider the same classification problem as in Chap. 5: for a given decision table T and a new object v it is required to generate a value of the decision attribute on v using values of conditional attributes on v. To this end, we divide the decision table T into a number of information systems Si, i ∈ Dec (T), where Dec (T) is the set of values of the decision attribute in T. For i ∈ Dec (T), the information system Si contains only objects (rows) of T with the value of the decision attribute equal to i.
UR - http://www.scopus.com/inward/record.url?scp=51849100751&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-85638-2_7
DO - 10.1007/978-3-540-85638-2_7
M3 - Conference contribution
AN - SCOPUS:51849100751
SN - 9783540856375
VL - 163
T3 - Studies in Computational Intelligence
SP - 87
EP - 97
BT - Inhibitory Rules in Data Analysis
ER -