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Conference Paper
An Improved Oversampling Method based on Neighborhood Kernel Density Estimation for Imbalanced Emotion Dataset
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- Authors
- Gague Kim, Seungeun Jung, Jiyoun Lim, Kyoung Ju Noh, Hyuntae Jeong
- Issue Date
- 2020-07
- Citation
- International Conference on Data Science (ICDATA) 2020, pp.1-12
- Language
- English
- Type
- Conference Paper
- Abstract
- Classification problem of imbalanced dataset is one of the main research topics. Imbalanced dataset where majority class outnumbers minority class is more difficult to handle than balanced dataset. The ADASYN approach has tried to solve this problem by generating more minority class samples for a few samples around the border between two classes. However, it is difficult to expect good classification with ADASYN when the imbalanced dataset contains noise samples instead of real minority class samples around the border. In this study, to overcome this problem, a new oversampling approach deals with the probability that a minority class sample belongs to dangerous set, not noise samples by using kernel density estimation. The proposed method generates appropriate synthetic samples to train well the learning model for minority class samples. Experiments are performed on ECG dataset collected for emotion classification. Finally, the experimental results show that our method improves the overall classification accuracy as well as recall rate for minority class.
- KSP Keywords
- Classification problems, Few samples, Imbalanced datasets, Kernel density estimation(KDE), Majority class, Minority class, Overall classification, Oversampling method, Recall rate, Research topics, classification accuracy