TY - GEN
T1 - Feature-weighted CBR with neural network for symbolic features
AU - Park, Sang Chan
AU - Kim, Jun Woo
AU - Im, Kwang Hyuk
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2006
Y1 - 2006
N2 - Case-based reasoning (CBR) is frequently applied to data mining with various objectives. Unfortunately, it suffers from the feature weighting problem. In this framework, similar case retrieval plays an important role, and the k-nearest neighbor (k-nn) method or its variants are widely used as the retrieval mechanism. However, the most important assumption of k-nn is that all of the features presented are equally important, which is not true in many practical applications. Many variants of k-nn have been proposed to assign higher weights to the more relevant features for case retrieval. Though many feature-weighted variants of k-nn have been reported to improve its retrieval accuracy on some tasks, few have been used in conjunction with the neural network learning. We propose CANSY, a feature-weighted CBR with neural network for symbolic features.
AB - Case-based reasoning (CBR) is frequently applied to data mining with various objectives. Unfortunately, it suffers from the feature weighting problem. In this framework, similar case retrieval plays an important role, and the k-nearest neighbor (k-nn) method or its variants are widely used as the retrieval mechanism. However, the most important assumption of k-nn is that all of the features presented are equally important, which is not true in many practical applications. Many variants of k-nn have been proposed to assign higher weights to the more relevant features for case retrieval. Though many feature-weighted variants of k-nn have been reported to improve its retrieval accuracy on some tasks, few have been used in conjunction with the neural network learning. We propose CANSY, a feature-weighted CBR with neural network for symbolic features.
UR - http://www.scopus.com/inward/record.url?scp=33749573892&partnerID=8YFLogxK
U2 - 10.1007/11816157_123
DO - 10.1007/11816157_123
M3 - Conference contribution
AN - SCOPUS:33749573892
SN - 3540372717
SN - 9783540372714
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 1012
EP - 1020
BT - International Conference on Intelligent Computing, ICIC 2006, Proceedings
PB - Springer Verlag
T2 - International Conference on Intelligent Computing, ICIC 2006
Y2 - 16 August 2006 through 19 August 2006
ER -