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Detecting Features from Confusion Matrices using Generalized Formal Concept Analysis

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URI: https://hdl.handle.net/10016/13110
ISBN: 978-3-642-13802-7
DOI: 10.1007/978-3-642-13803-4_47
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pelaez_detecting_HAIS_2010.pdf (526.85 KB)
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2010
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Springer
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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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Formal Concept Analysis, confusion matrix, contingency matrix, phonetics, biclustering
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Hybrid Artificial Intelligence Systems, 5th International Conference, HAIS 2010, San Sebastián, Spain, June 23-25, 2010. Proceedings, Part II. Springer, 2010, pp 375-382.
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