Abstract
Measuring the performance of a classifier properly is important to determine which classifier to use for an application domain. The comparison is not straightforward since different experiments may use different datasets, different class categories, and different data distribution, thus biasing the results. Many performance (correctness) measures have been described to facilitate the comparison of classification results. In this paper, we provide an overview of the performance measures for multiclass classification, and list the qualities expected in a good performance measure. We introduce a novel measure, probabilistic accuracy (Pacc), to compare multiclass classification results and make a comparative study of several measures and our proposed method based on different confusion matrices. Experimental results show that our proposed method is discriminative and highly correlated with accuracy compared to other measures. The web version of the software is available at http://sprite.cs.uah.edu/perf/.
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Sigdel, M., Aygün, R.S. (2013). Pacc - A Discriminative and Accuracy Correlated Measure for Assessment of Classification Results. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_22
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DOI: https://doi.org/10.1007/978-3-642-39712-7_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39711-0
Online ISBN: 978-3-642-39712-7
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