Towards Dynamic Feature Selection with Attention to Assist Banking Customers in Establishing a New Business
This work addresses the problem of dynamic feature selection for banking customers seeking to start a business, but it appears incremental as it applies an attention-based method to a specific domain without broad SOTA claims.
The paper tackles the challenge of extracting important features from banking and non-banking data to assist customers in establishing a new business, using an attention-based supervised feature selection approach, with experiments conducted on openly available datasets from Kaggle and UCI repositories.
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.