Assessments Metrics for Multi-class Imbalance Learning: A Preliminary Study

In this paper we study some of the most common global measures employed to measure the classifier performance on the multi-class imbalanced problems. The aim of this work consists of showing the relationship between global classifier performance (measure by global measures) and partial classifier performance, i.e., to determine if the results of global metrics match with the improved classifier performance over the minority classes. We have used five strategies to deal with the class imbalance problem over five real multi-class datasets on neural networks context.

[1]  Zhi-Hua Zhou,et al.  Ieee Transactions on Knowledge and Data Engineering 1 Training Cost-sensitive Neural Networks with Methods Addressing the Class Imbalance Problem , 2022 .

[2]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[3]  María José del Jesus,et al.  Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets , 2010, Pattern Recognit. Lett..

[4]  Grigorios Tsoumakas,et al.  Multi-Label Classification: An Overview , 2007, Int. J. Data Warehous. Min..

[5]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[6]  Roberto Alejo,et al.  A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios , 2013, Pattern Recognit. Lett..

[7]  Yi Lu Murphey,et al.  Multi-class pattern classification using neural networks , 2007, Pattern Recognit..

[8]  Lorenzo Bruzzone,et al.  Classification of imbalanced remote-sensing data by neural networks , 1997, Pattern Recognit. Lett..

[9]  Filiberto Pla,et al.  Prototype selection for the nearest neighbour rule through proximity graphs , 1997, Pattern Recognit. Lett..

[10]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[11]  Dennis L. Wilson,et al.  Asymptotic Properties of Nearest Neighbor Rules Using Edited Data , 1972, IEEE Trans. Syst. Man Cybern..

[12]  José Salvador Sánchez,et al.  Theoretical Analysis of a Performance Measure for Imbalanced Data , 2010, 2010 20th International Conference on Pattern Recognition.

[13]  José Hernández-Orallo,et al.  An experimental comparison of performance measures for classification , 2009, Pattern Recognit. Lett..

[14]  Foster J. Provost,et al.  Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction , 2003, J. Artif. Intell. Res..