TY - GEN
T1 - An extended fuzzy-kNN approach to solving class-imbalanced problems
AU - Xiong, Zhigang
AU - Watada, Junzo
AU - Xu, Zhenyuan
AU - Wang, Bo
AU - Tan, Shing Chiang
PY - 2014
Y1 - 2014
N2 - In this paper, for solving imbalanced classification problem, more attention is placed on data points in the boundary area between two classes. The fuzzy k-nearest neighbors algorithm, which has good performance in conventional classification problems, is adapted here to solve imbalanced classification problems, where G-mean accuracy is used to evaluate our proposal method and compare it with other approaches.
AB - In this paper, for solving imbalanced classification problem, more attention is placed on data points in the boundary area between two classes. The fuzzy k-nearest neighbors algorithm, which has good performance in conventional classification problems, is adapted here to solve imbalanced classification problems, where G-mean accuracy is used to evaluate our proposal method and compare it with other approaches.
KW - Fuzzy k-nearest neighbors algorithm
KW - Imbalanced dataset classification
UR - http://www.scopus.com/inward/record.url?scp=84902310912&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84902310912&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-405-3-200
DO - 10.3233/978-1-61499-405-3-200
M3 - Conference contribution
AN - SCOPUS:84902310912
SN - 9781614994046
VL - 262
T3 - Frontiers in Artificial Intelligence and Applications
SP - 200
EP - 209
BT - Frontiers in Artificial Intelligence and Applications
PB - IOS Press
ER -