A comparative study of the leading machine learning techniques and two new optimization algorithms

P. Baumann, D.S. Hochbaum and Y.T. Yang

European Journal of Operational Research, 2019, vol. 272, issue 3, 1041-1057

Abstract: We present here a computational study comparing the performance of leading machine learning techniques to that of recently developed graph-based combinatorial optimization algorithms (SNC and KSNC). The surprising result of this study is that SNC and KSNC consistently show the best or close to best performance in terms of their F1-scores, accuracy, and recall. Furthermore, the performance of SNC and KSNC is considerably more robust than that of the other algorithms; the others may perform well on average but tend to vary greatly across data sets. This demonstrates that combinatorial optimization techniques can be competitive as compared to state-of-the-art machine learning techniques. The code developed for SNC and KSNC is publicly available.

Keywords: Data mining; Supervised machine learning; Binary classification; Comparative study; Supervised normalized cut (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:272:y:2019:i:3:p:1041-1057

DOI: 10.1016/j.ejor.2018.07.009

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