TY - GEN
T1 - Purifying training data to improve performance of multi-label classification algorithms
AU - Kanj, Sawsan
AU - Abdallah, Fahed
AU - Denoux, Thierry
PY - 2012
Y1 - 2012
N2 - Multi-label classification assumes that each object in the training set is associated with a set of labels, and the goal is to assign labels to unseen instances. k-nearest neighbors based algorithms answer the multi-label problem by using inherent information given by the neighbors of the observation to classify. Due to several problems, like errors in the input vectors, or in their labels, this information may be wrong and might lead the multi-label algorithm to fail. In this paper, we propose a simple algorithm for editing out some training instances by voting of some metrics in order to purify the existing training sample. This purifying approach is adapted on the recently proposed evidential k-nearest neighbors for multi-label classification. Comparative experimental results on various data sets demonstrate the usefulness and effectiveness of our approach.
AB - Multi-label classification assumes that each object in the training set is associated with a set of labels, and the goal is to assign labels to unseen instances. k-nearest neighbors based algorithms answer the multi-label problem by using inherent information given by the neighbors of the observation to classify. Due to several problems, like errors in the input vectors, or in their labels, this information may be wrong and might lead the multi-label algorithm to fail. In this paper, we propose a simple algorithm for editing out some training instances by voting of some metrics in order to purify the existing training sample. This purifying approach is adapted on the recently proposed evidential k-nearest neighbors for multi-label classification. Comparative experimental results on various data sets demonstrate the usefulness and effectiveness of our approach.
UR - http://www.scopus.com/inward/record.url?scp=84867645129&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84867645129
SN - 9780982443859
T3 - 15th International Conference on Information Fusion, FUSION 2012
SP - 1784
EP - 1791
BT - 15th International Conference on Information Fusion, FUSION 2012
T2 - 15th International Conference on Information Fusion, FUSION 2012
Y2 - 7 September 2012 through 12 September 2012
ER -