Incremental Feature Learning For Infinite Data

Armin Sadreddin; Samira Sadaoui

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arXiv · 2108.02932

Incremental Feature Learning For Infinite Data

Armin Sadreddin·Samira Sadaoui

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Abstract

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.

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BibTeX RIS

Armin Sadreddin, Samira Sadaoui. 2021-08-06. Incremental Feature Learning For Infinite Data. https://arxiv.org/abs/2108.02932

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