Deep Discriminative Features Learning and Sampling for Imbalanced Data Problem
| Autor: | Chien-Liang Liu, Shin-Mu Tseng, Yi-Hsun Liu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
business.industry Computer science Deep learning Feature extraction 02 engineering and technology Machine learning computer.software_genre Synthetic data Multiclass classification ComputingMethodologies_PATTERNRECOGNITION Discriminative model Feature (computer vision) 020204 information systems 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | ICDM |
| DOI: | 10.1109/icdm.2018.00150 |
| Popis: | The imbalanced data problem occurs in many application domains and is considered to be a challenging problem in machine learning and data mining. Most resampling methods for synthetic data focus on minority class without considering the data distribution of major classes. In contrast to previous works, the proposed method considers both majority classes and minority classes to learn feature embeddings and utilizes appropriate loss functions to make feature embedding as discriminative as possible. The proposed method is a comprehensive framework and different deep learning feature extractors can be utilized for different domains. We conduct experiments utilizing seven numerical datasets and one image dataset based on multiclass classification tasks. The experimental results indicate that the proposed method provides accurate and stable results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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