Skip to main content
SpringerLink
Log in

College public sports culture practice based on decision tree algorithm

  • Original Article
  • Published:
Personal and Ubiquitous Computing Aims and scope Submit manuscript

Abstract

The public sports culture of colleges is based on the basic skills and strategies of the public sports culture curriculum. The study of public sports culture in colleges focuses on the unity and standardization of teaching forms, structures, contents, methods, assessments, and evaluations. This paper considers the various links that affect the public sports culture of colleges, identifies frequent item sets, and gains support by establishing support and confidence thresholds. The frequent item sets of the degrees and confidence with the rules generated by a decision tree algorithm are compared to identify the key factors that affect the actual effect. This paper fully considers the public sports culture of colleges to comprehensively analyze the relevant factors, verify and compare the rules generated by the decision tree algorithm, and identify the key factors that affect the actual effect. By an example verification, the method of this paper has certain guiding value for the study of public sports culture.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Jin M, Hua W, Qian Z (2018) Financial management and decision based on decision tree algorithm[J]. Wirel Pers Commun 2018(7):1–16

    Google Scholar 

  2. Liu X, Li B, Shen D (2017) Analysis of grain storage loss based on decision tree algorithm[J]. Procedia Computer Science 122(1):130–137

    Article  Google Scholar 

  3. Cai AP (2013) Improved edge detection algorithm based on decision tree[J]. Appl Mech Mater 321(4):1080–1084

    Article  Google Scholar 

  4. Shuai F, Li JQ (2013) Research on asset recognition algorithm of information security product based on decision tree algorithm[J]. Appl Mech Mater 397(4):2296–2300

    Article  Google Scholar 

  5. Ronowicz J, Thommes M, Kleinebudde P (2015) A data mining approach to optimize pellets manufacturing process based on a decision tree algorithm[J]. Eur J Pharm Sci 73(3):44–48

    Article  Google Scholar 

  6. Wang H, Tong W, Zhou Y (2018) Information classification algorithm based on decision tree optimization[J]. Clust Comput 2(26):1–10

    Google Scholar 

  7. Khryashchev VV, Lebedev AA, Priorov AL (2017) Enhancement of fast face detection algorithm based on a cascade of decision trees[J]. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 7(2):237–241

    Article  Google Scholar 

  8. Sun X (2017) Research on time series data mining algorithm based on Bayesian node incremental decision tree[J]. Clust Comput 2017(17):1–10

    Google Scholar 

  9. Liu ZY, Peng JX, Yang YK (2013) The application of decision tree algorithm in medical field based on cloud platform[J]. Appl Mech Mater 347(5):3397–3402

    Article  Google Scholar 

  10. Ran CL, Wang XJ (2015) Building a decision tree model for campus information score based on the algorithm C5.0[J]. Appl Mech Mater 719(2):805–811

    Article  Google Scholar 

  11. Patel N, Singh D (2015) An algorithm to construct decision tree for machine learning based on similarity factor[J]. Int J Comput Appl 111(10):22–26

    Google Scholar 

  12. Martinez RF, Okariz A, Ibarretxe J (2014) Use of decision tree models based on evolutionary algorithms for the morphological classification of reinforcing nano-particle aggregates[J]. Comput Mater Sci 92(5):102–113

    Article  Google Scholar 

  13. Wang J, Peng L, Ran R (2018) A short-term photovoltaic power prediction model based on the gradient boost decision tree[J]. Appl Sci 8(5):689

    Article  Google Scholar 

  14. Bai X, Wuliangha B, Hasiqiqige (2014) The study of the remote sensing image classification based on C5.0 algorithm of decision tree[J]. Remote Sens Technol Appl 29(2):338–343

    Google Scholar 

  15. Liu ZJ, Zhang J, Guang-Yi XU (2016) Classifying algorithm for concept drifting of uncertain data streams based on adapting fast decision tree algorithm[J]. Control and Decision 6(1):33–45

    Google Scholar 

  16. Duan W, Li MA, Xiang-Yang LU (2013) An improved algorithm of decision tree based on information gain and minimum distance classification[J]. Science Technology & Engineering 3(2):1–8

    Google Scholar 

  17. Liu S, Xu L, Li Q (2018) Fault diagnosis of water quality monitoring devices based on multiclass support vector machines and rule-based decision trees[J]. IEEE Access 9(99):1–15

    Google Scholar 

  18. Zou H, Qifeng WU, Zhang Y (2016) Performance evaluation method of scene matching algorithm based on decision tree[J]. Comput Digit Eng 6(5):23–34

    Google Scholar 

  19. Han WT, Peng Y, Hu HC (2013) Multiple decision tree algorithm for packet classification based on dynamic point Split[J]. J Electron Inf Technol 35(12):2985–2991

    Article  Google Scholar 

  20. Li Y, Yang L, Wang P (2017) Classification of Parkinson’s disease by decision tree based instance selection and ensemble learning algorithms[J]. J Med Imaging & Health Inform 7(2):73–86

    Google Scholar 

  21. Mccarthy MW, James DA, Lee JB (2015) Decision-tree-based human activity classification algorithm using single-channel foot-mounted gyroscope[J]. Electron Lett 51(9):675–676

    Article  Google Scholar 

  22. Min L (2018) A study on the influence of non-intelligence factors on college students’ English learning achievement based on C4.5 algorithm of decision tree[J]. Wirel Pers Commun 2018(4):1–10

    Google Scholar 

  23. Yang LI, Hai JG (2014) Facial expression recognition based on LBP and SVM decision tree[J]. Modern Computer 713-715(7):2152–2155

    Google Scholar 

  24. Zhe J, Shekhar S, Xun Z (2015) Focal-test-based spatial decision tree learning[J]. IEEE Trans Knowl Data Eng 27(6):1547–1559

    Article  Google Scholar 

  25. Rau CS, Wu SC, Chien PC (2017) Prediction of mortality in patients with isolated traumatic subarachnoid hemorrhage using a decision tree classifier: a retrospective analysis based on a trauma registry system:[J]. Int J Environ Res Public Health 14(11):1420–1432

    Article  Google Scholar 

  26. Rn HKK, Ji YKR, Kim JH (2016) Decision tree identified risk groups with high suicidal ideation in South Korea: a population-based study[J]. Public Health Nurs 33(2):99–106

    Article  Google Scholar 

  27. Rau CS, Wu SC, Chien PC (2018) Identification of pancreatic injury in patients with elevated amylase or lipase level using a decision tree classifier: a cross-sectional retrospective analysis in a level I trauma center[J]. Int J Environ Res Public Health 15(2):277–289

    Article  Google Scholar 

Download references

Funding

This work was supported by the Fundamental Research Funds for the Central Universities (No. JB2018MS066).

Author information

Authors and Affiliations

  1. North China Electric Power University, Beijing, 102206, China

    Shuping Xu & Lixin Liang

  2. Beijing College of Politics and Law, Beijing, 102628, China

    Chengbin Ji

Authors
  1. Shuping Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lixin Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chengbin Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuping Xu.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xu, S., Liang, L. & Ji, C. College public sports culture practice based on decision tree algorithm. Pers Ubiquit Comput 24, 207–221 (2020). https://doi.org/10.1007/s00779-019-01272-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00779-019-01272-7

Keywords

Search

Navigation