Abstract
We claim that the confusion matrices of multiclass problems can be analyzed by means of a generalization of Formal Concept Analysis to obtain symbolic information about the feature sets of the underlying classification task. We prove our claims by analyzing the confusion matrices of human speech perception experiments and comparing our results to those elicited by experts.
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Peláez-Moreno, C., Valverde-Albacete, F.J. (2010). Detecting Features from Confusion Matrices Using Generalized Formal Concept Analysis. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13803-4_47
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DOI: https://doi.org/10.1007/978-3-642-13803-4_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13802-7
Online ISBN: 978-3-642-13803-4
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