Classification through incremental max-min separability

Date

2011-05-01

Author

Bagirov, Adil M.
Ugon, Julien
Webb, Dean
Karasözen, Bülent

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Piecewise linear functions can be used to approximate non-linear decision boundaries between pattern classes. Piecewise linear boundaries are known to provide efficient real-time classifiers. However, they require a long training time. Finding piecewise linear boundaries between sets is a difficult optimization problem. Most approaches use heuristics to avoid solving this problem, which may lead to suboptimal piecewise linear boundaries. In this paper, we propose an algorithm for globally training hyperplanes using an incremental approach. Such an approach allows one to find a near global minimizer of the classification error function and to compute as few hyperplanes as needed for separating sets. We apply this algorithm for solving supervised data classification problems and report the results of numerical experiments on real-world data sets. These results demonstrate that the new algorithm requires a reasonable training time and its test set accuracy is consistently good on most data sets compared with mainstream classifiers.

Subject Keywords

Classification, Data mining, Data analysis, Supervised learning, Piecewise linear classifier

URI

https://hdl.handle.net/11511/29970

Journal

PATTERN ANALYSIS AND APPLICATIONS

DOI

https://doi.org/10.1007/s10044-010-0191-9

Collections

Graduate School of Applied Mathematics, Article

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Citation Formats

A. M. Bagirov, J. Ugon, D. Webb, and B. Karasözen, “Classification through incremental max-min separability,” PATTERN ANALYSIS AND APPLICATIONS, pp. 165–174, 2011, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/29970.