Mitic, Peter;
(2019)
A Metric Framework for quantifying Data
Concentration.
In: Yin, Hujun and Camacho, David and Tino, Peter and Tallón-Ballesteros, Antonio J and Menezes, Ronaldo and Allmendinger, Richard, (eds.)
Intelligent Data Engineering and Automated Learning – IDEAL 2019.
(pp. pp. 181-190).
Springer Nature: Cham, Switzerland.
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Abstract
Poor performance of artificial neural nets when applied to credit-related classification problems is investigated and contrasted with logistic regression classification. We propose that artificial neural nets are less successful because of the inherent structure of credit data rather than any particular aspect of the neural net structure. Three metrics are developed to rationalise the result with such data. The metrics exploit the distributional properties of the data to rationalise neural net results. They are used in conjunction with a variant of an established concentration measure that differentiates between class characteristics. The results are contrasted with those obtained using random data, and are compared with results obtained using logistic regression. We find, in general agreement with previous studies, that logistic regressions out-perform neural nets in the majority of cases. An approximate decision criterion is developed in order to explain adverse results.
| Type: | Proceedings paper |
|---|---|
| Title: | A Metric Framework for quantifying Data Concentration |
| Event: | 20th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2019 |
| ISBN-13: | 978-3-030-33616-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/978-3-030-33617-2_20 |
| Publisher version: | https://doi.org/10.1007/978-3-030-33617-2_20 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Copula; Hypersphere; Cluster; Herfindahl-Hirschman; HHI; Credit; Concentration; Decision criterion; Tensorflow; Neural Net |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10163342 |
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