Construction of Multiclass Classifier as Linear or Mixed Binary Programming Task

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F25%3A43933539" target="_blank" >RIV/60461373:22340/25:43933539 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21340/25:00387678
Result on the web
<a href="https://journals.pan.pl/Content/137602/PDF/art02.pdf" target="_blank" >https://journals.pan.pl/Content/137602/PDF/art02.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.24425/acs.2025.157142" target="_blank" >10.24425/acs.2025.157142</a>

Result language
angličtina
Original language name
Construction of Multiclass Classifier as Linear or Mixed Binary Programming Task
Original language description
Linear and mixed binary programming techniques, which arise from model linearity, are widely supported by advanced optimization solvers. In this paper, we present a comprehensive guide for transforming linear classifiers into linear or mixed binary programming tasks. Our approach employs widely used techniques, such as Kesler construction with either perfect or imperfect learning, and weight regularization using the L1 or L0 norm, enhanced by additional maximum weight constraint (i.e., L∞). Various linear optimization tasks are formulated based on performance measures such as accuracy and sensitivity. The classifiers are constructed using different weight penalizations and regularizations – specifically, the L0 norm, which yields mixed binary programming tasks with NP-hard complexity, and the L1 norm, which results in linear programming tasks with polynomial complexity, both with an additional maximum weight constraint. The proposed classifiers are compared on several UCI datasets (Iris Flower, Wine, and Seeds) and match or outperform Ridge and Lasso regression methods when applied to classification tasks.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10102 - Applied mathematics

Project
<a href="/en/project/LUC23138" target="_blank" >LUC23138: The role of inflammation in the progression of atherosclerosis studied by metabolomic and proteomic tools</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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