Learning Deep Landmarks for Imbalanced Classification.

Area of Science:

• Machine Learning
• Deep Learning
• Data Science

Background:

• Imbalanced learning is crucial for classification tasks where data distribution is uneven.
• Existing shallow learning methods rebalance samples before classification, which can be suboptimal.
• Deep learning models often struggle with imbalanced datasets, leading to biased predictions.

Purpose of the Study:

• To introduce a novel deep imbalanced learning framework called DELTA (learning DEep Landmarks in laTent spAce).
• To rebalance imbalanced samples within a deeply transformed latent space, enhancing data properties.
• To develop an end-to-end framework for joint feature learning, sample rebalancing, and discriminative learning.

Main Methods:

• DELTA rebalances samples in a latent space, creating compact and separable data points.
• The framework integrates feature learning, sample rebalancing, and discriminative learning simultaneously.
• DELTA supports advanced techniques like latent points oversampling and ensemble learning, and structured feature extraction.

Main Results:

• DELTA demonstrates effectiveness on benchmark datasets.
• The framework shows strong performance on real-world imbalanced tasks, including click-through-rate (CTR) prediction.
• Successful application to multi-class cell type classification and sequential sentiment analysis validates DELTA's versatility.

Conclusions:

• DELTA offers a powerful new approach to deep imbalanced learning by transforming data in a latent space.
• The framework's end-to-end nature and integration capabilities make it highly adaptable.
• DELTA significantly advances the state-of-the-art in handling imbalanced data across diverse applications.