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• W2953676618 abstract "Credit scoring is a well established and scrutinized domain within the artificial intelligence field of research and has direct implications in the functioning of financial institutions, by evaluating the risk of approving loans for different clients, which may or may not reimburse them in due time. It is the clients who fail to repay their debt that we are interested in predicting, which makes it a much more difficult task, since they form only a small minority of the total client count. From an input-output perspective, the problem can be stated as: given a set of client properties, such as age, marital status, loan duration, one must yield a 0-1 response variable, with 0 meaning good and 1, bad clients. Many techniques with high accuracy exist, such as artificial neural networks, but they behave as black box units. We add to this whole context the constraint that the output must be a concrete, tractable mathematical formula, which provides significant added value for a financial analyst. To this end, we present a means for evolving mathematical formulas using genetic programming coupled with Language Integrated Query expression trees, a feature present in the C# programming language." @default.
• W2953676618 created "2019-07-12" @default.
• W2953676618 creator A5036207638 @default.
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• W2953676618 date "2018-09-01" @default.
• W2953676618 modified "2023-09-23" @default.
• W2953676618 title "Evolving Mathematical Formulas using LINQ Expression Trees and Direct Applications to Credit Scoring" @default.
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• W2953676618 doi "https://doi.org/10.1109/synasc.2018.00069" @default.
• W2953676618 hasPublicationYear "2018" @default.
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