Abstract
In this work we study the behavior of the FARC-HD method when addressing multi-class classification problems using the One-vs-One (OVO) decomposition strategy. We will show that the confidences provided by FARC-HD (due to the use of the product in the inference process) are not suitable for this strategy. This problem implies that robust strategies like the weighted vote obtain poor results. For this reason, we propose two improvements: 1) the replacement of the product by greater aggregations whose output is independent of the number of elements to be aggregated and 2) the definition of a new aggregation strategy for the OVO methodology, which is based on the weighted vote, in which we only take into account the confidence of the predicted class in each base classifier. The experimental results show that the two proposed modifications have a positive impact on the performance of the classifier.
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Elkano, M., Galar, M., Sanz, J., Barrenechea, E., Herrera, F., Bustince, H. (2014). Improving the Performance of FARC-HD in Multi-class Classification Problems Using the One-Versus-One Strategy and an Adaptation of the Inference System. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-08852-5_31
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DOI: https://doi.org/10.1007/978-3-319-08852-5_31
Publisher Name: Springer, Cham
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