A semi-supervised segmentation algorithm as applied to k-means using information value.
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| Title: | A semi-supervised segmentation algorithm as applied to k-means using information value. |
|---|---|
| Authors: | Breed, D. G.1, Verstery, T.1 Tanja.Verster@nwu.ac.za, Terblanchez, S. E.1 |
| Source: | Orion. 2017, Vol. 33 Issue 2, p85-103. 19p. 5 Charts. |
| Subjects: | Supervised learning, Image segmentation, K-means clustering, Banking industry, Prediction models |
| Abstract: | Segmentation (or partitioning) of data for the purpose of enhancing predictive modelling is a well-established practice in the banking industry. Unsupervised and supervised approaches are the two main streams of segmentation and examples exist where the application of these techniques improved the performance of predictive models. Both these streams focus, however, on a single aspect (i.e. either target separation or independent variable distribution) and combining them may deliver better results in some instances. In this paper a semi-supervised segmentation algorithm is presented, which is based on k-means clustering and which applies information value for the purpose of informing the segmentation process. Simulated data are used to identify a few key characteristics that may cause one segmentation technique to outperform another. In the empirical study the newly proposed semi-supervised segmentation algorithm outperforms both an unsupervised and a supervised segmentation technique, when compared by using the Gini coefficient as performance measure of the resulting predictive models. [ABSTRACT FROM AUTHOR] |
| Copyright of Orion is the property of Operations Research Society of South Africa and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 126772780 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5784/33-2-568 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 19 StartPage: 85 Subjects: – SubjectFull: Supervised learning Type: general – SubjectFull: Image segmentation Type: general – SubjectFull: K-means clustering Type: general – SubjectFull: Banking industry Type: general – SubjectFull: Prediction models Type: general Titles: – TitleFull: A semi-supervised segmentation algorithm as applied to k-means using information value. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Breed, D. G. – PersonEntity: Name: NameFull: Verstery, T. – PersonEntity: Name: NameFull: Terblanchez, S. E. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: 2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 0259191X Numbering: – Type: volume Value: 33 – Type: issue Value: 2 Titles: – TitleFull: Orion Type: main |
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