{"id":1814,"date":"2026-03-30T00:18:36","date_gmt":"2026-03-29T16:18:36","guid":{"rendered":"https:\/\/iweb20wp-b205b.url.tku.edu.tw\/jase\/?post_type=tkuisotope&p=1814"},"modified":"2026-05-20T14:32:11","modified_gmt":"2026-05-20T06:32:11","slug":"adaptive-feature-selection-of-unbalanced-data-for-skiing-teaching","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=adaptive-feature-selection-of-unbalanced-data-for-skiing-teaching","title":{"rendered":"Adaptive Feature Selection of Unbalanced Data for Skiing Teaching"},"content":{"rendered":"\n
\n
\n

<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

\n

<\/i> Volume 29, Issue 3<\/a><\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Tao Feng<\/i><\/a><\/p>\n\n\n\n

Physical Education Teaching and Research Department, Harbin Finance University, Harbin 150030 China<\/p>\n<\/div>\n\n\n\n

\n

Received:\u00a0May 14, 2025
Accepted:\u00a0June 16, 2025
Publication Date:\u00a0March 30, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Proposed feature selection method for unbalanced data<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| https:\/\/doi.org\/10.6180\/jase.202603_29(3).0006<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

In skiing teaching, the data may show an unbalanced distribution. For example, the sample size of some common movements (such as straight downhill) may be much larger than that of some difficult movements (such as aerial spins). If the features are not selected, the model may overly rely on the features of common actions and ignore the features of difficult actions. Through the feature selection of unbalanced data, features that have significant influences on different actions (especially a few types of actions) can be screened out, thereby enhancing the recognition ability and generalization ability for various actions. The high-dimensional characteristics of the skiing dataset will reduce the classification effect of unbalanced learning. Aiming at the classification problem of high-dimensional unbalanced data, this paper proposes an adaptive feature selection method. This new algorithm combines embedded and wrapped feature selection methods and is capable of adaptively selecting the optimal features to form the feature space. Finally, the experimental results on the public imbalanced dataset show that the proposed algorithm effectively improves the classification performance of imbalanced data.<\/p>\n\n\n\n

Keywords:\u00a0skiing teaching; adaptive feature selection; unbalanced data; high-dimensional characteristics<\/em><\/p>\n\n\n\n

<\/div>\n\n\n\n
\n
    \n
  1. [1] N. Kurpiers and U. G. Kersting, (2017) \u201cThe one-ski-method\u2014effects of an alternative teaching approach on selected movement patterns in alpine skiing\u201d Cogent Social Sciences 3(1): 1275958. DOI: 10.1080\/23311886.2016.1275958.<\/li>\n
  2. [2] T. Li, J. Wang, K. Wiltos, and M. Wo\u017aniak, (2024) \u201cAn Intelligent Proofreading for Remote Skiing Actions Based on Variable Shape Basis\u201d Mobile Networks and Applications: 1\u201314. DOI: 10.1007\/s11036-024-02419-4.<\/li>\n
  3. [3] J. Lin, (2022) \u201cClassroom teaching design of alpine skiing based on virtual reality technology\u201d Mathematical Problems in Engineering 2022(1): 5721790. DOI: 10.1155\/2022\/5721790.<\/li>\n
  4. [4] Y. Sun, S. Yin, H. Li, L. Teng, and S. Karim, (2019) \u201cGPOGC: Gaussian pigeon-oriented graph clustering algorithm for social networks cluster\u201d IEEE Access 7: 99254\u201399262. DOI: 10.1109\/ACCESS.2019.2926816.<\/li>\n
  5. [5] C. Lin, C.-F. Tsai, and W.-C. Lin, (2023) \u201cTowards hybrid over-and under-sampling combination methods for class imbalanced datasets: an experimental study\u201d Artificial Intelligence Review 56(2): 845\u2013863. DOI: 10.1007\/s10462-022-10186-5.<\/li>\n
  6. [6] N. Wang and E. Zhao, (2024) \u201cA new method for feature selection based on weighted k-nearest neighborhood rough set\u201d Expert Systems with Applications 238: 122324. DOI: 10.1016\/j.eswa.2023.122324.<\/li>\n
  7. [7] J. Yu, L. Zhao, S. Yin, and M. Ivanovi\u0107, (2024) \u201cNews recommendation model based on encoder graph neural network and bat optimization in online social multimedia art education\u201d Computer Science and Information Systems 21(3): 989\u20131012. DOI: 10.2298\/CSIS231225025Y.<\/li>\n
  8. [8] J. Liu, Y. Lin, J. Du, H. Zhang, Z. Chen, and J. Zhang, (2023) \u201cASFS: A novel streaming feature selection for multi-label data based on neighborhood rough set\u201d Applied Intelligence 53(2): 1707\u20131724. DOI: 10.1007\/s10489-022-03366-x.<\/li>\n
  9. [9] B. Sang, W. Xu, H. Chen, and T. Li, (2023) \u201cActive anti-noise fuzzy dominance rough feature selection using adaptive k-nearest neighbors\u201d IEEE Transactions on Fuzzy Systems 31(11): 3944\u20133958. DOI: 10.1109\/TFUZZ.2023.3272316.<\/li>\n
  10. [10] I. Ni\u00f1o-Adan, I. Landa-Torres, E. Portillo, and D. Manjarres, (2022) \u201cInfluence of statistical normalization methods on K-Nearest Neighbours and K-Means in the context of industry 4.0\u201d Engineering Applications of Artificial Intelligence 111: 104807. DOI: 10.1016\/j.engappai.2022.104807.<\/li>\n
  11. [11] L. Sun, J. Zhang, W. Ding, and J. Xu, (2022) \u201cFeature reduction for imbalanced data classification using similarity-based feature clustering with adaptive weighted k-nearest neighbors\u201d Information Sciences 593: 591\u2013613. DOI: 10.1016\/j.ins.2022.02.004.<\/li>\n
  12. [12] S. Chormunge and S. Jena, (2018) \u201cCorrelation based feature selection with clustering for high dimensional data\u201d Journal of Electrical Systems and Information Technology 5(3): 542\u2013549. DOI: 10.1016\/j.jesit.2017.06.004.<\/li>\n
  13. [13] D. Dhabliya, K. Jain, M. Bargavi, A. Dhablia, J. R. R. Kumar, A. Gupta, S. Pramanik, et al. \u201cItem Selection Using K-Means and Cosine Similarity\u201d. In: AI-Driven Marketing Research and Data Analytics. IGI Global, 2024, 228\u2013244. DOI: 10.4018\/978-8-3693-2165-2.ch013.<\/li>\n
  14. [14] D. Thakur and S. Biswas, (2024) \u201cPermutation importance based modified guided regularized random forest in human activity recognition with smartphone\u201d Engineering Applications of Artificial Intelligence 129: 107681. DOI: 10.1016\/j.engappai.2023.107681.<\/li>\n
  15. [15] B. Elkari, Y. Chaibi, T. Kousksou, et al., (2024) \u201cRandom forest with feature selection and K-fold cross validation for predicting the electrical and thermal efficiencies of air based photovoltaic-thermal systems\u201d Energy Reports 12: 988\u2013999. DOI: 10.1016\/j.egyr.2024.07.002.<\/li>\n
  16. [16] Z. Runchi, X. Liguo, and W. Qin, (2023) \u201cAn ensemble credit scoring model based on logistic regression with heterogeneous balancing and weighting effects\u201d Expert Systems with Applications 212: 118732. DOI: 10.1016\/j.eswa.2022.118732.<\/li>\n
  17. [17] D. Lee and S. Suh, (2025) \u201cMeasuring Income and Wealth Inequality: A Note on the Gini Coefficient for Samples with Negative Values\u201d Social Indicators Research 176(3): 947\u2013965. DOI: 10.1007\/s11205-024-03488-4.<\/li>\n
  18. [18] V. Ignatenko, A. Surkov, and S. Koltcov, (2024) \u201cRandom forests with parametric entropy-based information gains for classification and regression problems\u201d PeerJ Computer Science 10: e1775. DOI: 10.7717\/peerj-cs.1775.<\/li>\n
  19. [19] S. P. Roy, A. Kasat, et al. \u201cDiabetic prediction with ensemble model and feature selection using information gain method\u201d. In: 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE. 2024, 1080\u20131085. DOI: 10.1109\/IDCIoT59759.2024.1046749.<\/li>\n
  20. [20] S. Srinivasan, S. Gunasekaran, S. K. Mathivanan, B. A. M. B, P. Jayagopal, and G. T. Dalu, (2023) \u201cAn active learning machine technique based prediction of cardiovascular heart disease from UCI-repository database\u201d Scientific reports 13(1): 13588. DOI: 10.1038\/s41598-023-40717-1.<\/li>\n
  21. [21] J. Qu and M. J. Zuo, (2010) \u201cSupport vector machine based data processing algorithm for wear degree classification of slurry pump systems\u201d Measurement 43(6): 781\u2013791. DOI: 10.1016\/j.measurement.2010.02.014.<\/li>\n
  22. [22] Q. Hu, D. Yu, J. Liu, and C. Wu, (2008) \u201cNeighborhood rough set based heterogeneous feature subset selection\u201d Information sciences 178(18): 3577\u20133594. DOI: 10.1016\/j.ins.2008.05.024.<\/li>\n
  23. [23] M.-S. Yang and Y. Nataliani, (2017) \u201cA feature-reduction fuzzy clustering algorithm based on feature-weighted entropy\u201d IEEE Transactions on Fuzzy Systems 26(2): 817\u2013835. DOI: 10.1109\/TFUZZ.2017.2692203.<\/li>\n
  24. [24] A. R. Yadav, R. S. Anand, M. Dewal, and S. Gupta. \u201cAnalysis and classification of hardwood species based on Coiflet DWT feature extraction and WEKA workbench\u201d. In: 2014 International Conference on Signal Processing and Integrated Networks (SPIN). IEEE. 2014, 9\u201313. DOI: 10.1109\/SPIN.2014.6776912.<\/li>\n
  25. [25] S. K. Behera and R. Dash, (2022) \u201cA novel feature selection technique for enhancing performance of unbalanced text classification problem\u201d Intelligent Decision Technologies 16(1): 51\u201369. DOI: 10.3233\/IDT-210057.<\/li>\n
  26. [26] F. Qin, A. M. Zain, K.-Q. Zhou, N. B. Yusup, D. D. Prasetya, R. A. Jalil, Z. Z. Abidin, M. Bahari, Y. Kamin, and M. A. Majid, (2025) \u201cHybrid Harmony Search Algorithm Integrating Differential Evolution and L\u00e9vy Flight for Engineering Optimization\u201d IEEE Access: DOI: 10.1109\/ACCESS.2025.3529714.<\/li>\n
  27. [27] H. T. Phuong and N. L. Giang, (2021) \u201cFuzzy distance-based filter-wrapper incremental algorithms for attribute reduction when adding or deleting attribute set\u201d Vietnam Journal of Science and Technology 59(2): 261\u2013274. DOI: 10.15625\/2525-2518\/59\/2\/15698.<\/li>\n<\/ol>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,15,6,118],"tags":[173],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:\u00a0 BibTeX | https:\/\/doi.org\/10.6180\/jase.202603_29(3).0006\u00a0\u00a0 Download PDF In skiing teaching, the data may show an unbalanced distribution. For…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/1814"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1814"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1814"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1814"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}