Abstract
In this paper, the neural network version of Extreme Learning Machine (ELM) is used as a base learner for an ensemble meta-algorithm which promotes diversity explicitly in the ELM loss function. The cost function proposed encourages orthogonality (scalar product) in the parameter space. Other ensemble-based meta-algorithms from AdaBoost family are used for comparison purposes. Both accuracy and diversity presented in our proposal are competitive, thus reinforcing the idea of introducing diversity explicitly.
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Notes
- 1.
The dot product is squared aiming to focus solely on the direction of the vector.
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Perales-González, C., Carbonero-Ruz, M., Becerra-Alonso, D., Fernández-Navarro, F. (2018). A Preliminary Study of Diversity in Extreme Learning Machines Ensembles. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_25
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DOI: https://doi.org/10.1007/978-3-319-92639-1_25
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