Multi-objective decision in machine learning
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Universidade Federal de Minas Gerais
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Artigo de periódico
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This work presents a novel approach for decision-making 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.
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Inteligência artificial, Aprendizado do computador, Sistemas especialistas (Computação), Recuperação da informação
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knowledge is effectively applied to model a decision mechanism in the Pareto-optimal set, the method MOBJ-htnn was developed with the intention of enabling the use of the knowledge about the inherent imprecision of the data acquisition process
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https://link.springer.com/article/10.1007/s40313-016-0295-6