IMCP: A Python']Python package for imbalanced and multiclass data classifier performance comparison

被引:0
|
作者
Aguilar-Ruiz, Jesus S. [1 ]
Michalak, Marcin [2 ]
Wrobel, Lukasz [2 ]
机构
[1] Pablo de Olavide Univ, Sch Engn, ES-41013 Seville, Spain
[2] Silesian Univeristy Technol, Dept Comp Networks & Syst, Ul Akademicka 16, PL-44100 Gliwice, Poland
关键词
Classification; Multiclass data; Imbalanced data; Performance; ROC curve; !text type='Python']Python[!/text;
D O I
10.1016/j.softx.2024.101877
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The Multiclass Classification Performance (MCP) curve is an innovative method to visualize the performance of a classifier for multiclass datasets. On the other hand, the Imbalanced Multiclass Classification Performance (IMCP) curve is a novel approach to visualizing classifier performance on multiclass datasets that exhibit class imbalance, i.e. the proportions of (two or more) class labels are unequal. We have developed an opensource Python package that encompasses the functionality required to calculate and visualize these two novel classification performance measures, along with providing the calculation of the area under the curves. The MCP and IMCP curves offer advantages over the traditional ROC (Receiver Operating Characteristic) curve when dealing with multiclass and imbalanced datasets, respectively. They provide more informative insights into classifier behavior, especially in scenarios involving multiple classes or uneven class distribution.
引用
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页数:5
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