Deep Imbalance Learning via Fuzzy Transition and Prototypical Learning (imFTP, Information Sciences 2024)

This repository contains the Pytorch implementations of the paper submitted to Information Sciences 2024:

Yaxin Hou, Weiping Ding, Chongsheng Zhang. Deep Imbalance Learning via Fuzzy Transition and Prototypical Learning. Information Sciences 2024. Paper

Introduction

This work (imFTP) aims to

Abstract:

Prerequisites

All codes are written by Python 3.8 with:

Operating System: Windows 10
torch 1.13.0
torchaudio 0.13.0
torchvision 0.14.0
pandas 1.5.2
scikit-learn 1.1.3
imbalanced-learn 0.9.1
numpy 1.23.5
openpyxl 3.0.10

Code structure description

   imFTP
   ├──data
   │   ├──original_data  
   │   └──spilted_data
   │
   ├──model
   │   ├──model.png
   │   └──model.py
   │
   ├──result
   │
   ├──trained_model
   │   
   ├──utils
   │   ├──log.py
   │   ├──dataset.py
   │   ├──split_data.py
   │   └──transformer.py 
   │
   ├──imFTP_TRAIN.py
   ├──imFTP_TEST.py
   └──README.MD

Train

To train a classifier for class-imbalanced data:

python imFTP_TRAIN.py --dataset mfcc

Test

To test the classifier with the trained model:

 python imFTP_TEST.py --dataset mfcc

Our Trained models

Under the folder “trained_model”, we have uploaded our trained models for the mfcc dataset.

Classification model is at ./trained_model/

Citation

If you find our method useful, please consider citing our paper:

@inproceedings{imFTP2023,
  title={Deep Imbalance Learning via Fuzzy Transition and Prototypical Learning},
  author={Yaxin Hou and Weiping Dingand Chongsheng Zhang},
  booktitle={Information Sciences},
  year={2024},
}

yaxinhou/imFTP