# Incremental Feature Learning For Infinite Data

arXiv: https://arxiv.org/abs/2108.02932 · HTML: https://mlsanity.com/paper/2108.02932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 6 Aug 2021.

## Builds on (most cited first, 1 of 1)

- [Deep Learning Methods for Credit Card Fraud Detection](https://mlsanity.com/paper/2012.03754/) (2020)

## Built on by (most cited first, 1 of 1)

- [Feature Incremental Clustering with Generalization Bounds](https://mlsanity.com/paper/2603.21590/) (2026)

## Abstract (arXiv)

This study addresses the actual behavior of the credit-card fraud detection environment where financial transactions containing sensitive data must not be amassed in an enormous amount to conduct learning. We introduce a new adaptive learning approach that adjusts frequently and efficiently to new transaction chunks; each chunk is discarded after each incremental training step. Our approach combines transfer learning and incremental feature learning. The former improves the feature relevancy for subsequent chunks, and the latter, a new paradigm, increases accuracy during training by determining the optimal network architecture dynamically for each new chunk. The architectures of past incremental approaches are fixed; thus, the accuracy may not improve with new chunks. We show the effectiveness and superiority of our approach experimentally on an actual fraud dataset.