Zero-Shot Machine Learning Technique for Classification of Multi-User Big Data Workloads

Genkin, Mikhail

doi:10.1109/bigdata50022.2020.9378023

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…In classification, a dataset is said to be imbalanced when there is a class with a smaller amount of data compared to other classes. The class with a larger amount of data is called the majority class while the class with a small amount of data is called the minority class [14]. The imbalanced problem in the vehicle route classification process has become a challenge in the classification process and attracted the attention of a number of researchers.…”

Section: Introductionmentioning

confidence: 99%

Vehicle routing problem optimization with machine learning in imbalanced classification vehicle route data

Novelan,

Efendi,

Sihombing

et al. 2023

EEJET

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The object of this research is a combinatorial optimization problem arising in the problem of the route of goods delivery vehicles. In this study, the proposed method for solving combinatorial optimization problems consists of several stages: Data Cleaning, Data Preprocessing, K-NN and Cavacity Vehicle Routing Problem model. The results show that the machine learning approach can optimise combinatorial optimization problems, especially in generating vehicle route points and delivery capacity. The characteristics in determining vehicle routes by considering latitude and longitude points. This research builds a framework and implements it in a multi-class optimization model to reduce overfitting and misclassification results caused by unbalanced multiclassification from the influence of the number of 'nodes' on vehicle routes with machine learning. The purpose of the model in general is to gain an understanding of the mechanism in the problem so that it can classify unbalanced vehicle route data based on Jalur Nugraha Ekakurir delivery routes. So that with the availability of the model can be a model in determining vehicle routes based on the capacity limit of the number of shipments of goods. The results of research with machine learning models and vehicle routing problems with testing K values 11, 13, 15. Where it has a percentage of K=11 accuracy 57.3265 % and K=13 accuracy 57.3265 % and K=15 accuracy 81.8645 %. From the test results with odd K values have better accuracy and the K 15 K=15 value is better with a percentage of 81.8645 % compared to K 11 K=11, and 13 K=13. As a result, the developed model in terms of accuracy of the cavacity vehicle routing problem model has an accuracy of 93.80 % and the time series achieves an average precision of 93.31 % and with a recall value of 93.80 %. The results obtained can be useful in developing a more modern model, Cavacity Vehicle Routing Problem with Machine Learning

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Section: Introductionmentioning

confidence: 99%

Vehicle routing problem optimization with machine learning in imbalanced classification vehicle route data

Novelan,

Efendi,

Sihombing

et al. 2023

EEJET

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“…• ZSL Workload Classifier. This component, described in Chapter 8 and in [14] re-uses the WorkloadClassifier class, and introduces the WorkloadSynthesizer component. This component needs to be trained off-line.…”

Section: Autonomic Architecture For Big Datamentioning

confidence: 99%

Autonomic Performance Optimization of Big Data Workloads

Genkin¹

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Zero-Shot Machine Learning Technique for Classification of Multi-User Big Data Workloads

Cited by 2 publications

References 14 publications

Vehicle routing problem optimization with machine learning in imbalanced classification vehicle route data

Vehicle routing problem optimization with machine learning in imbalanced classification vehicle route data

Autonomic Performance Optimization of Big Data Workloads

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