Prototype generation method using a growing self-organizing map applied to the banking sector

Description

In fields like security risk analysis, Fast Moving Consumer Goods, Internet of Things, or the banking sector, it is necessary to deal with large datasets containing a great list of variables. In these situations, the analysis becomes intricate and computationally expensive, so data reduction techniques play an important role. Prototype generation methods provide a reduced dataset with the same properties as the original. GSOMs (growing self-organizing maps) reduce the data size without the need for prefixing the number of neurons needed to represent the input space. To the best of the authors' knowledge, this is the first time that the GSOM is applied for reduction and generation of prototypes, posing an advantage over their predecessors, the SOMs (self-organizing maps), which do not have the automatic growth feature. This work addresses the use of a GSOM to reduce the number of prototypes to use in a 1-NN (1 nearest neighbor) classifier. The proposed methodology is applied to an income dataset for testing and a large bank dataset that contain classifications into two different groups. The 1-NN classifier is used to obtain predictions using the nodes of the GSOM as prototypes. This article demonstrates that GSOMs save a significant amount of time in obtaining nearly the same validation results as SOMs by comparing the classifications obtained in the bank dataset. The results show data reductions of more than 99%, and accuracies greater than 80% for the income dataset and 74% for the bank dataset.

Abstract

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons. org/licenses/by/4.0/.

Additional details