Multi-objective Decision in Machine Learning

Medeiros, Talles Henrique de; Rocha, Honovan Paz; Torres, Frank Sill; Takahashi, Ricardo H. C.; Braga, Antônio de Pádua

doi:10.1007/s40313-016-0295-6

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…These algorithms usually produce accurate results, at high computational costs, which may even improve if data are scattered. Therefore, this method has been used to treat uncertainty in studies, such as [ 83 , 84 ].…”

Section: Computational Methods For Decision-making Under Uncertaintymentioning

confidence: 99%

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Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

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Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.

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Section: Computational Methods For Decision-making Under Uncertaintymentioning

confidence: 99%

“…Optimization problems. When it is not feasible to find an optimal solution for uncertain problems, a solution that satisfies problem constraints may be selected, as described in [ 84 , 110 , 111 , 112 , 113 , 114 ].…”

Section: Computational Methods For Decision-making Under Uncertaintymentioning

confidence: 99%

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Alzate-Mejía

Santos-Boada

Amazonas

2021

Sensors

View full text Add to dashboard Cite

show abstract

A TOPSIS multi-criteria decision method-based intelligent recurrent wavelet CMAC control system design for MIMO uncertain nonlinear systems

Lin

2018

Neural Comput & Applic

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Self-Organizing Recurrent Wavelet Fuzzy Neural Network-Based Control System Design for MIMO Uncertain Nonlinear Systems Using TOPSIS Method

Lin

2018

Int. J. Fuzzy Syst.

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Multi-objective Decision in Machine Learning

Cited by 6 publications

References 13 publications

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

Decision-Making under Uncertainty for the Deployment of Future Hyperconnected Networks: A Survey

A TOPSIS multi-criteria decision method-based intelligent recurrent wavelet CMAC control system design for MIMO uncertain nonlinear systems

Self-Organizing Recurrent Wavelet Fuzzy Neural Network-Based Control System Design for MIMO Uncertain Nonlinear Systems Using TOPSIS Method

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