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Profit-Driven Classification with Machine Learning

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Part of the Credit Research Centre Seminar Series.

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Overview

Support Vector Machine (SVM) is a powerful classification approach that can be useful for decision support systems given its superior performance compared to traditional strategies, like logistic regression.

This method, however, is not designed to take into account profit-related issues. In particular, it cannot identify the most relevant features used for the classifier construction, or incorporate profit measures in the classifier construction.

In this work we propose a profit-driven approach for classifier construction and simultaneous variable selection based on SVM. The main goal is to incorporate business-related information such as the variables' acquisition costs, the type I and II error costs, and the profit generated by correctly classified instances into the modelling process.

Our proposal incorporates a group penalty function in the SVM formulation in order to simultaneously penalise the variables that belong to the same group (the L-infinity norm), assuming that companies often receive groups of related variables for a given cost rather than receiving them individually. This function is combined with the Tikhonov and Lasso regularisation functions, leading to two SVM formulations for classification and embedded feature selection.

A case-study of a Chilean bank is presented. Credits are granted to micro-entrepreneurs based on information from five different data sources. Our proposal concludes that the best solutions in terms of profit, are achieved using one or two cheap data sources, without the need of expensive interviews. Additionally, important managerial insights are gained into the application thanks to the identification of the relevant variables.

About the Speaker

Sebastián Maldonado is currently Full Professor at the Department of Management Control and Information Systems, School of Economics and Business, University of Chile.

He received his BS and MS degrees from the University of Chile, in 2007, and his PhD from the University of Chile, in 2011. His research interests include statistical learning, data mining, and business analytics.

Sebastián Maldonado has published more than 70 Web of Science (WOS) papers in the last 10 years. He is currently Chair of the Chilean Chapter of the IEEE Computational Intelligence Society, and was President of the Chilean Operational Research Society (ICHIO) from 2017 to 2019. He was also a member of the Engineering Group for the The National Fund for Scientific and Technological Development (FONDECYT) program from 2018 to 2020.

FONDECYT, aims to encourage and promote the development of basic scientific and technological research. It is the main public fund of scientific and technological research in Chile.

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