TLiSVM (Triple Linear SVM Weight) for dimensionality reduction

Buathong, Wipawan

doi:10.1109/iccse.2016.7581593

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Staff category 1 represents "management personnel," 2 represents "technical personnel," and 3 represents "skilled personnel." After the above data processing, data formats that meet the requirements of the decision tree algorithm were obtained, as shown in Table 2, where Label is the target variable of decision tree regression analysis, and Feature is the data attributeto be classified [7] .…”

Section: Association and Conversion Of Attendance Datamentioning

confidence: 99%

Research and Regression Analysis of Enterprise Attendance Data Based on Big Data Technology

Yao¹,

Bian²,

Ji³

et al. 2023

Proceedings of the 2nd International Conference on Information, Control and Automation, ICICA 2022, December 2-4, 2022, Chongqi

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The traditional database technology can hardly complete full data analysis on mass accumulated attendance data for the enterprise over the years, while big data technology is regarded as a better way to achieve it. After the study on a data-driven attendance management system, this paper proposed a decision tree regression analysis method based on the Spark platform, analyzing mass attendance data in a short time and generating a decision tree to show what affects employee attendance. The decision tree has certain reference significance for the monitoring and warning of employee attendance and employee management decision. This paper detailed the process of dataparallel processing and the regression analysis method of employee attendance by the Classification and Regression Tree (CART) algorithm and compared the efficiency of traditional database technology and parallel data processing for employee attendance data analysis, which verified the effectiveness of the method.

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Section: Association and Conversion Of Attendance Datamentioning

confidence: 99%