Abstract
When combining classifiers, we aggregate the output of different machine learning methods, and base our decision on the aggregated probability values instead of the individual ones. In the phoneme classification task of speech recognition, small excerpts of speech need to be identified as one of the pre-defined phonemes; but the probability value assigned to each possible phoneme also hold valuable information. This is why, when combining classifier output in this task, we must use a combination scheme which can aggregate the output probability values of the basic classifiers in a robust way. We tested the representative uninorms for this task, and were able to significantly outperform all the basic classifiers tested.
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Gosztolya, G., Dombi, J. (2014). Applying Representative Uninorms for Phonetic Classifier Combination. In: Torra, V., Narukawa, Y., Endo, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2014. Lecture Notes in Computer Science(), vol 8825. Springer, Cham. https://doi.org/10.1007/978-3-319-12054-6_16
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DOI: https://doi.org/10.1007/978-3-319-12054-6_16
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