Weight Modification Method for Performance Evaluation Model of Computing System Based on Bayes' Theorem

Da-liang, Fang; Lan, Yuqing

doi:10.1145/3180374.3180378

Cited by 1 publication

(2 citation statements)

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“…It also significantly improves classification accuracy as well as reduces the computational time of the algorithm. These advantages of Bayes’ theorem should be incorporated and applied in different domains and subjects to observe the results in various areas of studies [24, 25, 31]. On the other hand, DM techniques have been used to classify the employees.…”

Section: Related Workmentioning

confidence: 99%

“…This integration approach produces a 1% increase in performance accuracy, precision, and recall in the predictive model, respectively. Fang and Lan [31] applied Bayes’ theorem to modify the weight of the important factors that most influence the proposed performance evaluation model of the computing system. They found that Bayes' theorem significantly improves the overall accuracy of the evaluation performance model of the computing system [31].…”

Section: Related Workmentioning

confidence: 99%

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Determining the best‐fit programmers using Bayes’ theorem and artificial neural network

Prathan

2020

IET softw.

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A data mining-based technique is proposed for the selection and employment of the best-fit programmers to meet the needs of software companies. The proposed technique incorporates Bayes' theorem and artificial neural network (ANN). The datasets used were from two software companies (Company 1 and Company 2) in India, covering the years 2010-2015. Bayes' theorem is used for identifying the prognostic attributes of the best-fit programmers, while the ANN classifier is used for predicting the best-fit programmers. Using a confusion matrix, the ANN classifier performance is 97.2 and 87.3%, 95.8 and 54.5%, and 100 and 75% with regard to accuracy, precision, and recall on the two test datasets of Company 1 and Company 2, respectively. The results show that the technique is effective for predicting the best-fit programmers. Software companies can use this technique in their recruitment and selection process to determine the best-fit employees for the programmer posts. The proposed technique can also be adapted for application in other disciplines such as sports, education, etc., to identify the most suitable person to fill a relevant position.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%