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Polygon Area Metric for Classifier Evaluation

version 2.0.2 (2.9 KB) by Önder Aydemir
Aydemir, O. A New Performance Evaluation Metric for Classifiers: Polygon Area Metric. J Classif (2020). https://doi.org/10.1007/s00357-020-0

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Updated 20 Jun 2020

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Classifier performance assessment (CPA) is a challenging task for pattern recognition. In recent years, various CPA metrics have been developed to help assess the performance of classifiers. Although the classification accuracy (CA), which is the most popular metric in pattern recognition area, works well if the classes have equal number of samples, it fails to evaluate the recognition performance of each class when the classes have different number of samples. To overcome this problem, researchers have developed various metrics including sensitivity, specificity, area under curve, Jaccard index, Kappa, and F measure except CA. Giving many evaluation metrics for assessing the performance of classifiers make large tables possible. Additionally, when comparing classifiers with each other, while a classifier might be more successful on a metric, it may have poor performance for the other metrics. Hence, such kinds of situations make it difficult to track results and compare classifiers. This study proposes a stable and profound knowledge criterion that allows the performance of a classifier to be evaluated with only a single metric called as polygon area metric (PAM). Thus, classifier performance can be easily evaluated without the need for several metrics.

Cite As

Önder Aydemir (2020). Polygon Area Metric for Classifier Evaluation (https://www.mathworks.com/matlabcentral/fileexchange/74136-polygon-area-metric-for-classifier-evaluation), MATLAB Central File Exchange. Retrieved .

Aydemir, Onder. “A New Performance Evaluation Metric for Classifiers: Polygon Area Metric.” Journal of Classification, Springer Science and Business Media LLC, Jan. 2020, doi:10.1007/s00357-020-09362-5.

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Comments and Ratings (13)

Ural Akincioglu

Chang hsiung

thanks a lot

Önder Aydemir

Dear Chang Hsiung, If you don't have Bioinformatics Toolbox, you might passivate the line of "classperf" and provide to the function TP, FN, FP, TN, CorrectRate, Sensitivity and Specificity values on your own. Please don't hesitate to write your comments for any other errors if exists.

Chang hsiung

unfortunately I got the following error msg:
'classperf' requires Bioinformatics Toolbox.

Chang hsiung

Ercument YILMAZ

Leb Lebi

Leb Lebi

ozlempolat

Yavuz KABLAN

amir naser

Ercument YILMAZ

Ehab Salahat

Updates

2.0.2

NUMERICALLY LARGER CLASS IS AUTOMATICALLY ASSIGNED AS PositiveClass

2.0.1

Calculation of AUC is added

2.0.0

Calculation of AUC is added

MATLAB Release Compatibility
Created with R2019b
Compatible with any release
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