# Towards Dynamic Feature Selection with Attention to Assist Banking Customers in Establishing a New Business

arXiv: https://arxiv.org/abs/2105.03852 · HTML: https://mlsanity.com/paper/2105.03852/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 134 references that are in the map.
- First posted on arXiv 9 May 2021.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [Meta-Learning with Latent Embedding Optimization](https://mlsanity.com/paper/1807.05960/) (2018)
- [Cross-stitch Networks for Multi-task Learning](https://mlsanity.com/paper/1604.03539/) (2016)
- [A Survey on Deep Transfer Learning](https://mlsanity.com/paper/1808.01974/) (2018)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Wasserstein regularization for sparse multi-task regression](https://mlsanity.com/paper/1805.07833/) (2018)
- [Enabling the Analysis of Personality Aspects in Recommender Systems](https://mlsanity.com/paper/2001.04825/) (2020)
- [Learning to Transfer](https://mlsanity.com/paper/1708.05629/) (2017)
- [Data Curation APIs](https://mlsanity.com/paper/1612.03277/) (2016)

## Abstract (arXiv)

Establishing a new business may involve Knowledge acquisition in various areas, from personal to business and marketing sources. This task is challenging as it requires examining various data islands to uncover hidden patterns and unknown correlations such as purchasing behavior, consumer buying signals, and demographic and socioeconomic attributes of different locations. This paper introduces a novel framework for extracting and identifying important features from banking and non-banking data sources to address this challenge. We present an attention-based supervised feature selection approach to select important and relevant features which contribute most to the customer's query regarding establishing a new business. We report on the experiment conducted on an openly available dataset created from Kaggle and the UCI machine learning repositories.