TY - GEN
T1 - Meta-learning for selecting a multi-label classification algorithm
AU - Chekina, Lena
AU - Rokach, Lior
AU - Shapira, Bracha
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Although various algorithms for multi-label classification have been developed in recent years, there is little, if any, information as to when each method is beneficial. The main goal of this paper is to compare the classification performance of several multi-label algorithms and to develop a set of rules or tools that will help in selecting the optimal algorithm according to a specific dataset and target evaluation measure. We utilize a meta-learning approach allowing fast automatic selection of the most appropriate algorithm for an unseen dataset based on its descriptive characteristics. We also define a list of characteristics specific for multi-label datasets. The experimental results indicate the applicability and usefulness of the meta-learning approach.
AB - Although various algorithms for multi-label classification have been developed in recent years, there is little, if any, information as to when each method is beneficial. The main goal of this paper is to compare the classification performance of several multi-label algorithms and to develop a set of rules or tools that will help in selecting the optimal algorithm according to a specific dataset and target evaluation measure. We utilize a meta-learning approach allowing fast automatic selection of the most appropriate algorithm for an unseen dataset based on its descriptive characteristics. We also define a list of characteristics specific for multi-label datasets. The experimental results indicate the applicability and usefulness of the meta-learning approach.
KW - Dataset characteristics
KW - Evaluation measures
KW - Meta-learning
KW - Multi-label classification
UR - https://www.scopus.com/pages/publications/84857188857
U2 - 10.1109/ICDMW.2011.118
DO - 10.1109/ICDMW.2011.118
M3 - Conference contribution
AN - SCOPUS:84857188857
SN - 9780769544090
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 220
EP - 227
BT - Proceedings - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
T2 - 11th IEEE International Conference on Data Mining Workshops, ICDMW 2011
Y2 - 11 December 2011 through 11 December 2011
ER -