Abstract
Ensemble approaches have revealed remarkable abilities to tackle different learning challenges, namely in dynamic scenarios with concept drift, e.g. in social networks, as Twitter. Several efforts have been engaged in defining strategies to combine the models that constitute an ensemble. In this work, we investigate the effect of using different metrics for combining ensembles’ models, specifically performance-based metrics. We propose five performance combining metrics, having in mind that we may take advantage of diversity in classifiers, as their individual performance takes a leading role in defining their contribution to the ensemble. Experimental results on a Twitter dataset, artificially timestamped, suggest that using performance metrics to combine the models that constitute an ensemble can introduce relevant improvements in the overall ensemble performance.
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It is also financed by national funding via the Foundation for Science and Technology and by the European Regional Development Fund (FEDER), through the COMPETE 2020 - Operational Program for Competitiveness and Internationalization (POCI).
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Costa, J., Silva, C., Antunes, M., Ribeiro, B. (2017). Performance Metrics for Model Fusion in Twitter Data Drifts. In: Alexandre, L., Salvador Sánchez, J., Rodrigues, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2017. Lecture Notes in Computer Science(), vol 10255. Springer, Cham. https://doi.org/10.1007/978-3-319-58838-4_2
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DOI: https://doi.org/10.1007/978-3-319-58838-4_2
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