We consider the problem of measuring the performances associated with members of a given group of homogeneous individuals. We provide both an analysis, relying on Machine Learning paradigms, along with a numerical experience based on three conceptually different real applications. A keynote aspect in the proposed approach is represented by our data–driven framework, where guidelines for evaluating individuals’ performance are derived from the data associated to the entire group. This makes our analysis and the relative outcomes quite versatile, so that a number of real problems can be studied in view of the proposed general perspective.

Data Analytics and Machine Learning paradigm to gauge performances combining classification, ranking and sorting for system analysis

ANDREA PONTIGGIA
Membro del Collaboration Group
;
GIOVANNI FASANO
Membro del Collaboration Group
2021

Abstract

We consider the problem of measuring the performances associated with members of a given group of homogeneous individuals. We provide both an analysis, relying on Machine Learning paradigms, along with a numerical experience based on three conceptually different real applications. A keynote aspect in the proposed approach is represented by our data–driven framework, where guidelines for evaluating individuals’ performance are derived from the data associated to the entire group. This makes our analysis and the relative outcomes quite versatile, so that a number of real problems can be studied in view of the proposed general perspective.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10278/3741624
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