TY - GEN
T1 - Partial covers, reducts and decision rules with weights
AU - Moshkov, Mikhail Ju
AU - Piliszczuk, Marcin
AU - Zielosko, Beata
PY - 2008
Y1 - 2008
N2 - In this chapter, we study the case, where each subset, used for covering, has its own weight, and we should minimize the total weight of subsets in partial cover. The same situation is with partial reducts and decision rules: each conditional attribute has its own weight, and we should minimize the total weight of attributes in partial reduct or decision rule. If weight of each attribute characterizes time complexity of attribute value computation, then we try to minimize total time complexity of computation of attributes from partial reduct or partial decision rule. If weight characterizes a risk of attribute value computation (as in medical or technical diagnosis), then we try to minimize total risk, etc.
AB - In this chapter, we study the case, where each subset, used for covering, has its own weight, and we should minimize the total weight of subsets in partial cover. The same situation is with partial reducts and decision rules: each conditional attribute has its own weight, and we should minimize the total weight of attributes in partial reduct or decision rule. If weight of each attribute characterizes time complexity of attribute value computation, then we try to minimize total time complexity of computation of attributes from partial reduct or partial decision rule. If weight characterizes a risk of attribute value computation (as in medical or technical diagnosis), then we try to minimize total risk, etc.
UR - http://www.scopus.com/inward/record.url?scp=51649084831&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-69029-0_3
DO - 10.1007/978-3-540-69029-0_3
M3 - Conference contribution
AN - SCOPUS:51649084831
SN - 9783540690276
VL - 145
T3 - Studies in Computational Intelligence
SP - 51
EP - 96
BT - Partial Covers, Reducts and Decision Rules in Rough Sets
ER -