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.

**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

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

Thanks goes to these wonderful people (emoji key):

Chuang Zhao 🤔 💻

Nanlin Liu 🤔 💻

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