Pareto-based multi-objective optimization for classification in data mining

Kamila, Narendra Kumar; Jena, Lambodar; Bhuyan, Hemanta Kumar

doi:10.1007/s10586-016-0643-0

Cited by 29 publications

(8 citation statements)

References 36 publications

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“…e method was then implemented in power networks and the effectiveness of the approach was demonstrated. Literature [41] also makes use of the Pareto-based MOO technique, which produces more effective outcomes when dealing with unique categorization challenges. A new adaptive multiobjective adaptive intelligent search and optimization algorithm based on the Gray Wolf optimizer is proposed by Yildirim Gungor in literature [42] when dealing with multiobjective optimization problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Passive Power Filter Optimization Problem Based on Adaptive Multipopulation NSGA-II and CRITIC-TOPSIS

Yuan

Liu

2022

Mathematical Problems in Engineering

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The best configuration of passive filters in extra-high voltage systems is investigated in this study using a two-stage multiobjective decision-making (MODM) framework. A collection of Pareto solutions is found using an adaptive multipopulation-modified nondominated ranking genetic algorithm (AMP NSGA-II) in the first stage. The goals taken into account include the least fundamental reactive power compensation losses, overall cost and maintenance cost, and simultaneous minimum harmonic current distortion rate. The objective weights are determined and the optimum solution is chosen in the second stage using the criteria importance interrelationship (CRITIC) and similarity ranking preference technique (TOPSIS). A number of examples show how the suggested method performs more effectively than the conventional and well-known algorithm and can identify the relationship between the objective functions, indicating that the suggested scheme has better superiority in filters and has a promising future in the solution of multiobjective problems.

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Section: Literature Reviewmentioning

confidence: 99%

Passive Power Filter Optimization Problem Based on Adaptive Multipopulation NSGA-II and CRITIC-TOPSIS

Yuan

Liu

2022

Mathematical Problems in Engineering

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“…where m represents the number of objective functions of the MOP. Each objective can be described mathematically as a set of equality or inequality equations as [23]:…”

Section: Multi-objective Problems (Mops)mentioning

confidence: 99%

“…PO is a common method that has been widely used to solve MOPs [6,8,12,23]. It tries to satisfy the whole objectives simultaneously.…”

Section: Pareto Optimizationmentioning

confidence: 99%

A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients

et al. 2021

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The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.

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“…Based on fuzzy random variables conditional expectation was used in which two fuzzy sets were generated using Borel set that helped to determine sub-feature within certain interval. Kamila et al [34] dealt with association of sub-features for noble classification and potentially interesting sub-features in multi-objective optimisation for classification were used to strengthen the concept of Pareto-based multi-objective optimisation. The present work used 'RELIEF' [35,36], a robust feature selection algorithm, which can assess the quality of the feature subsets and attempts to improve classification accuracy through selection of only high-quality features.…”

Section: Feature Quality Assessment Through Relief Algorithmmentioning

confidence: 99%

Structured approach to downsize data administration and improve fault recognition accuracy for large power systems

Jana¹,

De²

2020

IET Generation, Transmission & Distribution

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Pareto-based multi-objective optimization for classification in data mining

Cited by 29 publications

References 36 publications

Passive Power Filter Optimization Problem Based on Adaptive Multipopulation NSGA-II and CRITIC-TOPSIS

Passive Power Filter Optimization Problem Based on Adaptive Multipopulation NSGA-II and CRITIC-TOPSIS

A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients

Structured approach to downsize data administration and improve fault recognition accuracy for large power systems

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