.png){kind=icon}
Return
FIAO: Feature Information Aggregation Oversampling for imbalanced data classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2024.111774.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Classification performance often deteriorates when machine learning algorithms are trained on imbalanced data. Although oversampling methods have been successfully employed to address imbalanced data, existing approaches have limitations such as information loss, difficulty in parameter selection, and boundary effects when using and calculating nearest neighbors and densities. Therefore, this study introduces a novel oversampling method called Feature Information Aggregation Oversampling (FIAO). FIAO leverages feature information, including feature importance, feature density, and standard deviation, to guide the oversampling process. Initially, the feature information is employed to partition features into suitable intervals for feature generation. Subsequently, features are generated within these intervals. Finally, the generated features are integrated into the minority class data to achieve effective oversampling. The key advantage of FIAO lies in its ability to fully exploit the intrinsic information carried by the features themselves, thus circumventing issues related to parameter selection and boundary effects. To assess its efficacy, extensive experiments were conducted on 12 widely used benchmark datasets, comparing the performance of the proposed method against 10 popular resampling methods across four commonly used classifiers. The experimental results show that the proposed FIAO method shows ideal results in multiple application scenarios and achieves optimal performance.
Keywords:
Imbalanced datasets
Oversampling method
Feature information aggregation
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
Cited Papers
Arsenic removal from aqueous solutions by adsorption using novel MIL-53(Fe) as a highly efficient adsorbent.png)
RSC Advances
RSC AdvancesIF0
PREAI
ShareSaveShareSaveA multi-objective mutation-based dynamic Harris Hawks optimization for botnet detection in IoTINTERNET OF THINGS
INTERNET OF THINGSIF7.6
PREAI
ShareSaveOn the effectiveness of preprocessing methods when dealing with different levels of class imbalance
PREAI
ShareSaveShareSave
ShareSave
View more