TY - GEN
T1 - Fuzzy multi-label learning under veristic variables
AU - Younes, Zoulficar
AU - Abdallah, Fahed
AU - Denœux, Thierry
PY - 2010
Y1 - 2010
N2 - Multi-label learning is increasingly required by many applications where instances may belong to several classes at the same time. In this paper, we propose a fuzzy k-nearest neighbor method for multi-label classification using the veristic variable framework. Veristic variables are variables that can assume simultaneously multiple values with different degrees. In multi-label learning, class labels can be considered as veristic variables since each instance can belong simultaneously to more than one class. Several applications on benchmark datasets demonstrate the efficiency of our approach.
AB - Multi-label learning is increasingly required by many applications where instances may belong to several classes at the same time. In this paper, we propose a fuzzy k-nearest neighbor method for multi-label classification using the veristic variable framework. Veristic variables are variables that can assume simultaneously multiple values with different degrees. In multi-label learning, class labels can be considered as veristic variables since each instance can belong simultaneously to more than one class. Several applications on benchmark datasets demonstrate the efficiency of our approach.
UR - http://www.scopus.com/inward/record.url?scp=78549264786&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.2010.5584079
DO - 10.1109/FUZZY.2010.5584079
M3 - Conference contribution
AN - SCOPUS:78549264786
SN - 9781424469208
T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
Y2 - 18 July 2010 through 23 July 2010
ER -