An Ensemble Learning Approach with Gradient Resampling for Class-imbalanced problems

About Our Work

In this paper, we propose a new approach from the sample-level classification difficulty identifying, sampling and ensemble learning. Accordingly, we design an ensemble approach in pipe with sample-level gradient resampling, i.e., Balanced Cascade with Filters (BCWF). Before that, as a preliminary exploration, we first design a Hard Examples Mining Algorithm (HEM) to explore the gradient distribution of classification difficulty of samples and identify the hard examples.

The figure below gives an overview of the our framework.

Requirements

**Main dependencies: **

• Python (>=3.5)
• pandas (>=0.23.4)
• numpy (>=1.11)
• scikit-learn (>=0.20.1)
• imbalanced-learn (=0.5.0, optional, for baseline methods)

To install requirements, run:

pip install -r requirements.txt

Usage

A typical usage example:

# Define model
model_class = BcwfH(dataset_name, T=15) 
# Model calculation
model_class.apply_all()
# Metrics
metrics = model_class.display()

You can run .py file too.

Here is an example:

# Run Model
python main.py
# Hyper-test
python hyper.py
# Get comparsion
python result.py

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Chuang Zhao 🤔 💻

Nanlin Liu 🤔 💻

This project follows the all-contributors specification. Contributions of any kind welcome!