# Scalable Twin Neural Networks for Classification of Unbalanced Data

arXiv: https://arxiv.org/abs/1705.00347 · HTML: https://mlsanity.com/paper/1705.00347/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2017.

## Built on by (most cited first, 2 of 2)

- [Comprehensive Review On Twin Support Vector Machines](https://mlsanity.com/paper/2105.00336/) (2021)
- [Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification](https://mlsanity.com/paper/2303.02505/) (2023)

## Abstract (arXiv)

Twin Support Vector Machines (TWSVMs) have emerged an efficient alternative to Support Vector Machines (SVM) for learning from imbalanced datasets. The TWSVM learns two non-parallel classifying hyperplanes by solving a couple of smaller sized problems. However, it is unsuitable for large datasets, as it involves matrix operations. In this paper, we discuss a Twin Neural Network (Twin NN) architecture for learning from large unbalanced datasets. The Twin NN also learns an optimal feature map, allowing for better discrimination between classes. We also present an extension of this network architecture for multiclass datasets. Results presented in the paper demonstrate that the Twin NN generalizes well and scales well on large unbalanced datasets.