A hybrid machine learning-optimization approach to pricing and train formation problem under demand uncertainty

Yousefi, Atiye; Pishvaee, Mir Saman

doi:10.1051/ro/2022052

Cited by 6 publications

(4 citation statements)

References 54 publications

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“…Then, using the data in the dataset, it conducts numerical simulation on the three algorithms, and compares each evaluation indicator of the algorithm. According to the comparison results, it is verified that the CFA based on hybrid ML optimization has good reliability and stability [3][4].…”

Section: Machine Learning Theory and Practicementioning

confidence: 88%

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Collaborative Filtering Algorithm based on Hybrid Machine Learning Optimization

2020

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Under the influence of the mobile Internet era, users' patience is increasingly limited. Future recommendation algorithms should quickly respond to users' urgent needs to save users' time. Under the background of big data, how to ensure the relatively low complexity and high accuracy of information push and display is a very valuable topic. Therefore, this paper discusses and analyzes the collaborative filtering algorithm (CFA) based on hybrid machine learning (ML) optimization. In this paper, the research background and significance of CFA are first described, including the development status of collaborative filtering recommendation algorithm, the research status of association rule recommendation algorithm and particle swarm optimization algorithm, and the problems in collaborative filtering recommendation algorithm. A new hybrid CFA model is proposed based on the hybrid ML optimization. This paper designs a simulation table of three recommendation algorithms to verify the proposed CFA. The experiment shows that the recall rate of the CFA proposed in this paper based on the hybrid ML optimization is not lower than other recommendation algorithms, which verifies the effectiveness of this algorithm.

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Section: Machine Learning Theory and Practicementioning

confidence: 88%

“…That is to say, the design in the previous stage prepares for the preprocessing in the next stage. The constructed model can be used in the next stage, and the recommended list can also be given to optimize the calculation in the next stage [10].…”

Section: Flow Typementioning

confidence: 99%

Collaborative Filtering Algorithm based on Hybrid Machine Learning Optimization

2020

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“…When people's daily habits were replaced by sofas, Jianjian by coffee cups, and imperial calendars by month cards, arts and crafts were no longer limited to handicrafts. Instead, it was combined with the machine industry and took on various characteristics of "modernity" [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Research and practice on the application of computer virtual technology in arts and crafts nowadays

Huang,

Jiang

2023

Applied Mathematics and Nonlinear Sciences

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With the gradual maturation of virtual computer technology, it is increasingly used in real life to represent its effects realistically. The connection between arts and crafts and digital technology is getting closer. To bring new opportunities to the development of arts and crafts, virtual reality technology and augmented reality technology is integrated to establish a 3D arts and crafts virtual design model with visualized interactive arts and crafts design as the core. In the visualized virtual scene with the support of a craft knowledge base, the coordinates of virtual design tools, original objects inside the tools, and other points are derived according to the pick-up points. By repeating “delete current scene - generate new scene”, the design of arts and crafts is realized. After the verification of properties, performance, and satisfaction, the process error of the virtual design model is only 1.57mm, the average design cycle is 5.91 hours, and the labor cost, pre-preparation cost, and design cost are 214.66 RMB, 100 RMB, and 20 RMB respectively, which are significantly lower than the traditional design mode. Furthermore, the average scoring value of designers for different forms of arts and crafts is greater than 8 points, which is more satisfying with the application effect of the model. The model is connected to design practice with the help of virtual technology, which not only improves work efficiency and saves material and labor consumption but also facilitates the optimization of design solutions and the extension of solution serialization.

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“…However, manual intervention is inefficient, and not all scenarios are guided by experienced human experts. Automated Machine Learning (AutoML for short) came into being [1][2]. AutoML is the combination of automation technology and machine learning.…”

Section: Introductionmentioning

confidence: 99%

Theory and Practice of Super Parameter Optimization for Machine Learning Algorithm

2020

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Before the rise of large machine learning algorithms, most people manually adjusted the super parameters of the model by relying on experience. However, with the increasing complexity of the model, this method obviously cannot meet the needs. This paper mainly studies the theory and practice of super parameter optimization of machine learning algorithm. This thesis proposes a regression-based hyperparameter optimization algorithm that has the same data-based optimization algorithm as the optimization algorithm Bayesian. The optimization algorithm is based on the Gaussian regression process. In addition to being affected by the super parameters of the kernel function in the process of GP regression fitting, the calculation amount of the algorithm will also increase significantly. The experimental results show that, compared to the optimization algorithm, the parameter optimization results of this algorithm are similar to those of the optimization algorithm.

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A hybrid machine learning-optimization approach to pricing and train formation problem under demand uncertainty

Cited by 6 publications

References 54 publications

Collaborative Filtering Algorithm based on Hybrid Machine Learning Optimization

Collaborative Filtering Algorithm based on Hybrid Machine Learning Optimization

Research and practice on the application of computer virtual technology in arts and crafts nowadays

Theory and Practice of Super Parameter Optimization for Machine Learning Algorithm

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