The feature subset task can be cast as a multiobjective discrete optimization problem. In this work, we study the search algorithm component of a feature subset selection method. We propose an algorithm based on the threshold accepting method, extended to the multi-objective framework by an appropriate definition of the acceptance rule. The method is used in the task of identifying relevant subsets of features in a Web bot recognition problem, where automated software agents on the Web are identified by analyzing the stream of HTTP requests to a Web server.

Feature selection: A multi-objective stochastic optimization approach / S. Rovetta, G. Suchacka, A. Cabri, F. Masulli - In: 2020 IEEE 6th International Conference on Optimization and Applications (ICOA)[s.l] : IEEE, 2020. - ISBN 978-1-7281-6654-4. - pp. 1-5 (( Intervento presentato al 6. convegno International Conference on Optimization and Applications, ICOA 2020 tenutosi a Beni Mellal nel 2020 [10.1109/ICOA49421.2020.9094478].

Feature selection: A multi-objective stochastic optimization approach

A. Cabri
Penultimo
;
2020

Abstract

The feature subset task can be cast as a multiobjective discrete optimization problem. In this work, we study the search algorithm component of a feature subset selection method. We propose an algorithm based on the threshold accepting method, extended to the multi-objective framework by an appropriate definition of the acceptance rule. The method is used in the task of identifying relevant subsets of features in a Web bot recognition problem, where automated software agents on the Web are identified by analyzing the stream of HTTP requests to a Web server.
feature selection; multiobjective optimization; stochastic optimization; subset selection; threshold accepting
Settore INF/01 - Informatica
2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/955223
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