Abstract
Although over 90 oversampling approaches have been developed in the imbalance learning domain, most of the empirical study and application work are still based on the “classical” resampling techniques. In this paper, several experiments on 19 benchmark datasets are set up to study the efficiency of six powerful oversampling approaches, including both “classical” and new ones. According to our experimental results, oversampling techniques that consider the minority class distribution (new ones) perform better in most cases and RACOG gives the best performance among the six reviewed approaches. We further validate our conclusion on our real-world inspired vehicle datasets and also find applying oversampling techniques can improve the performance by around 10%. In addition, seven data complexity measures are considered for the initial purpose of investigating the relationship between data complexity measures and the choice of resampling techniques. Although no obvious relationship can be abstracted in our experiments, we find F1v value, a measure for evaluating the overlap which most researchers ignore, has a strong negative correlation with the potential AUC value (after resampling).
Original language | English |
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Title of host publication | Advances in Knowledge Discovery and Data Mining - 24th Pacific-Asia Conference, PAKDD 2020, Proceedings |
Editors | Hady W. Lauw, Ee-Peng Lim, Raymond Chi-Wing Wong, Alexandros Ntoulas, See-Kiong Ng, Sinno Jialin Pan |
Publisher | Springer Vieweg |
Pages | 84-96 |
Number of pages | 13 |
ISBN (Print) | 9783030474355 |
DOIs | |
Publication status | Published - 2020 |
Externally published | Yes |
Event | 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 - Singapore, Singapore Duration: 11 May 2020 → 14 May 2020 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12085 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2020 |
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Country/Territory | Singapore |
City | Singapore |
Period | 11/05/20 → 14/05/20 |
Bibliographical note
Publisher Copyright:© Springer Nature Switzerland AG 2020.
Keywords
- Class imbalance
- Data complexity measures
- Minority class distribution
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science