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A Strategy on Selecting Performance Metrics for Classifier Evaluation

A Strategy on Selecting Performance Metrics for Classifier Evaluation

Yangguang Liu, Yangming Zhou, Shiting Wen, Chaogang Tang
Copyright: © 2014 |Volume: 6 |Issue: 4 |Pages: 16
ISSN: 1937-9412|EISSN: 1937-9404|EISBN13: 9781466655607|DOI: 10.4018/IJMCMC.2014100102
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MLA

Liu, Yangguang, et al. "A Strategy on Selecting Performance Metrics for Classifier Evaluation." IJMCMC vol.6, no.4 2014: pp.20-35. http://doi.org/10.4018/IJMCMC.2014100102

APA

Liu, Y., Zhou, Y., Wen, S., & Tang, C. (2014). A Strategy on Selecting Performance Metrics for Classifier Evaluation. International Journal of Mobile Computing and Multimedia Communications (IJMCMC), 6(4), 20-35. http://doi.org/10.4018/IJMCMC.2014100102

Chicago

Liu, Yangguang, et al. "A Strategy on Selecting Performance Metrics for Classifier Evaluation," International Journal of Mobile Computing and Multimedia Communications (IJMCMC) 6, no.4: 20-35. http://doi.org/10.4018/IJMCMC.2014100102

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Abstract

The evaluation of classifiers' performances plays a critical role in construction and selection of classification model. Although many performance metrics have been proposed in machine learning community, no general guidelines are available among practitioners regarding which metric to be selected for evaluating a classifier's performance. In this paper, we attempt to provide practitioners with a strategy on selecting performance metrics for classifier evaluation. Firstly, the authors investigate seven widely used performance metrics, namely classification accuracy, F-measure, kappa statistic, root mean square error, mean absolute error, the area under the receiver operating curve, and the area under the precision-recall curve. Secondly, the authors resort to using Pearson linear correlation and Spearman rank correlation to analyses the potential relationship among these seven metrics. Experimental results show that these commonly used metrics can be divided into three groups, and all metrics within a given group are highly correlated but less correlated with metrics from different groups.

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