Multi-objective decision in machine learning.
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Date
2016
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Abstract
Thiswork presents a novel approach for decisionmaking
for multi-objective binary classification problems.
The purpose of the decision process is to select within a set of
Pareto-optimal solutions, one model that minimizes the structural
risk (generalization error). This new approach utilizes
a kind of prior knowledge that, if available, allows the selection
of a model that better represents the problem in question.
Prior knowledge about the imprecisions of the collected data
enables the identification of the region of equivalent solutions
within the set of Pareto-optimal solutions. Results for
binary classification problems with sets of synthetic and real
data indicate equal or better performance in terms of decision
efficiency compared to similar approaches.
Description
Keywords
Machine learning, Multi-objective optimization, Decision-making, Classification
Citation
MEDEIROS, T. H. de et al. Multi-objective decision in machine learning. Journal of Control, Automation and Electrical Systems, v. 4, p. 217–227, 2016. Disponível em: <https://link.springer.com/article/10.1007/s40313-016-0295-6>. Acesso em: 02 out. 2017.