6533b7d6fe1ef96bd126663d

RESEARCH PRODUCT

Feature selection: A multi-objective stochastic optimization approach

Alberto CabriGrażyna SuchackaFrancesco MasulliStefano Rovetta

subject

Web serverLinear programmingthreshold acceptingComputer scienceFeature extractionFeature selectionstochastic optimizationcomputer.software_genreMulti-objective optimizationfeature selection; multiobjective optimization; stochastic optimization; subset selection; threshold acceptingfeature selectionsubset selectionFeature (computer vision)Search algorithmStochastic optimizationmultiobjective optimizationData miningcomputer

description

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.

yearjournalcountryeditionlanguage
2020-04-01
10.1109/icoa49421.2020.9094478https://hdl.handle.net/11567/1058398