Ensemble learning: A study on different variants of the dynamic selection approach

• Type: Article in International Conference Proceedings Book
• Date: 2009

Authors

João Mendes Moreira (Author) FEUP; Alípio Mário Jorge (Author) FCUP; Carlos Soares (Author) FEP; Jorge Freire de Sousa (Author) FEUP

Conference proceedings International

Title: Machine Learning and Data Mining in Pattern Recognition

Pages: 191-205

6th International Conference on Machine Learning and Data Mining in Pattern Recognition (MLDM 2009)

Leipzig, Germany, 23-25 July, 2009

Indexing

ISI Web of Knowledge - 16 Citations

ISI Web of Science

Scopus - 19 Citations

INSPEC

Scientific classification

CORDIS: Physical sciences > Computer science > Cybernetics > Artificial intelligence

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-003-R7M

DOI: 10.1007/978-3-642-03070-3_15

Abstract (EN): Integration methods for ensemble learning can use two different approaches: combination or selection. The combination approach (also called fusion) consists on the combination of the predictions obtained by different models in the ensemble to obtain the final ensemble predication. The selection approach selects one (or more) models from the ensemble according to the prediction performance of these models on similar data from the validation set. Usually, the method to select similar data is the k-nearest neighbors with the Euclidean distance. In this paper we discuss other approaches to obtain similar data for the regression problem. We show that using similarity measures according to the target values improves results. We also show that selecting dynamically several models for the prediction task increases prediction accuracy comparing to the selection of just one model.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15