TY - GEN
T1 - Network of experts
T2 - 26th International Conference on Neural Information Processing, ICONIP 2019
AU - Gomes, Heitor Murilo
AU - Bifet, Albert
AU - Fournier-Viger, Philippe
AU - Granatyr, Jones
AU - Read, Jesse
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Ensemble classifiers are a promising approach for data stream classification. Though, diversity influences the performance of ensemble classifiers, current studies do not take advantage of relations between component classifiers to improve their performance. This paper addresses this issue by proposing a new kind of ensemble learner for data stream classification, which explicitly defines relations between component classifiers. These relations are then used in various ways, e.g., to combine the decisions of component models. The hypothesis is that an ensemble learner can yield accurate predictions in a streaming environment based on a structural analysis of a weighted network of its component models. Implications, limitations and benefits of this assumption, are discussed. A formal description of a network-based ensemble for data streams is presented, and an algorithm that implements it, named Network of Experts (NetEx). Empirical experiments show that NetEx’s accuracy and processing time are competitive with state-of-the-art ensembles.
AB - Ensemble classifiers are a promising approach for data stream classification. Though, diversity influences the performance of ensemble classifiers, current studies do not take advantage of relations between component classifiers to improve their performance. This paper addresses this issue by proposing a new kind of ensemble learner for data stream classification, which explicitly defines relations between component classifiers. These relations are then used in various ways, e.g., to combine the decisions of component models. The hypothesis is that an ensemble learner can yield accurate predictions in a streaming environment based on a structural analysis of a weighted network of its component models. Implications, limitations and benefits of this assumption, are discussed. A formal description of a network-based ensemble for data streams is presented, and an algorithm that implements it, named Network of Experts (NetEx). Empirical experiments show that NetEx’s accuracy and processing time are competitive with state-of-the-art ensembles.
KW - Classification
KW - Data stream
KW - Ensemble learning
U2 - 10.1007/978-3-030-36708-4_58
DO - 10.1007/978-3-030-36708-4_58
M3 - Conference contribution
AN - SCOPUS:85077504275
SN - 9783030367077
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 704
EP - 716
BT - Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
A2 - Gedeon, Tom
A2 - Wong, Kok Wai
A2 - Lee, Minho
PB - Springer
Y2 - 12 December 2019 through 15 December 2019
ER -