# Scalable Twin Neural Networks for Classification of Unbalanced Data

## TL;DR

This paper introduces a scalable Twin Neural Network architecture designed for classifying large, unbalanced datasets, improving upon traditional Twin SVMs by enabling better discrimination and handling multiclass problems.

## Contribution

The paper proposes a novel Twin Neural Network architecture that scales efficiently for large unbalanced datasets and extends to multiclass classification, with an optimal feature mapping.

## Key findings

- Demonstrates good generalization on large unbalanced datasets
- Scales well compared to traditional TWSVMs
- Effective extension to multiclass classification

## Abstract

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.