{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T17:49:58Z","timestamp":1784224198164,"version":"3.55.0"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T00:00:00Z","timestamp":1618876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["20720200094"],"award-info":[{"award-number":["20720200094"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s00521-021-05962-3","type":"journal-article","created":{"date-parts":[[2021,4,20]],"date-time":"2021-04-20T06:03:24Z","timestamp":1618898604000},"page":"11517-11529","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["A feature weighted support vector machine and artificial neural network algorithm for academic course performance prediction"],"prefix":"10.1007","volume":"35","author":[{"given":"Chenxi","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junsheng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinling","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jane","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kathy","family":"Clawson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonghong","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,20]]},"reference":[{"key":"5962_CR1","doi-asserted-by":"crossref","unstructured":"Kurzweil M, Wu DD (2015) Case study: building a pathway to student success at Georgia State University","DOI":"10.18665\/sr.221053"},{"key":"5962_CR2","unstructured":"Ministry of Education of the People\u2019s Republic of China (2016) Promotion rate of graduates of regular school by levels. http:\/\/en.moe.gov.cn\/Resources\/Statistics\/edu_stat_2015\/2015_en01\/201610\/t20161012_284485.html. Accessed 4 Feb 2021"},{"key":"5962_CR3","unstructured":"Ministry of Education (2020) Composition of students in senior secondary schools\u2014Ministry of Education of the People\u2019s Republic of China. http:\/\/en.moe.gov.cn\/documents\/statistics\/2018\/national\/201908\/t20190812_394224.html. Accessed 4 Feb 2021"},{"key":"5962_CR4","doi-asserted-by":"crossref","unstructured":"Kloft M, Stiehler F, Zheng Z, Pinkwart N (2015) Predicting MOOC dropout over weeks using machine learning methods. In: Proceedings of the EMNLP 2014 workshop on analysis of large scale social interaction in MOOCs. Association for Computational Linguistics, Stroudsburg, pp 60\u201365","DOI":"10.3115\/v1\/W14-4111"},{"key":"5962_CR5","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/bf00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273\u2013297. https:\/\/doi.org\/10.1007\/bf00994018","journal-title":"Mach Learn"},{"key":"5962_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/8404653","volume":"2019","author":"J Chen","year":"2019","unstructured":"Chen J, Feng J, Sun X et al (2019) MOOC dropout prediction using a hybrid algorithm based on decision tree and extreme learning machine. Math Probl Eng 2019:1\u201311. https:\/\/doi.org\/10.1155\/2019\/8404653","journal-title":"Math Probl Eng"},{"key":"5962_CR7","doi-asserted-by":"crossref","unstructured":"Sweeney M, Lester J, Rangwala H (2015) Next-term student grade prediction. In: Proceedings of 2015 IEEE international conference on Big Data, IEEE Big Data 2015. IEEE, pp 970\u2013975","DOI":"10.1109\/BigData.2015.7363847"},{"key":"5962_CR8","doi-asserted-by":"publisher","unstructured":"Sweeney M, Rangwala H, Lester J, Johri A (2016) Next-term student performance prediction: a recommender systems approach, pp 1\u201327. https:\/\/doi.org\/10.5281\/zenodo.3554603","DOI":"10.5281\/zenodo.3554603"},{"key":"5962_CR9","doi-asserted-by":"crossref","unstructured":"Morsy S, Karypis G (2017) Cumulative knowledge-based regression models for next-term grade prediction. In: Chawla N, Wang W (eds) Proceedings of the 17th SIAM international conference on data mining, SDM 2017. Society for Industrial and Applied Mathematics, Philadelphia, pp 552\u2013560","DOI":"10.1137\/1.9781611974973.62"},{"key":"5962_CR10","unstructured":"Ren Z, Ning X, Lan AS, Rangwala H (2019) Grade prediction based on cumulative knowledge and co-taken courses. In: EDM 2019\u2014Proceedings of 12th international conference on educational data mining, pp 158\u2013167"},{"key":"5962_CR11","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s10115-018-1185-y","volume":"58","author":"SM Saqlain","year":"2019","unstructured":"Saqlain SM, Sher M, Shah FA et al (2019) Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines. Knowl Inf Syst 58:139\u2013167. https:\/\/doi.org\/10.1007\/s10115-018-1185-y","journal-title":"Knowl Inf Syst"},{"key":"5962_CR12","doi-asserted-by":"crossref","unstructured":"Arsad PM, Buniyamin N, Manan JLA (2014) Prediction of engineering students\u2019 academic performance using artificial neural network and linear regression: a comparison. In: 2013 IEEE 5th International conference on engineering education: aligning engineering education with industrial needs for nation development, ICEED 2013. IEEE, pp 43\u201348","DOI":"10.1109\/ICEED.2013.6908300"},{"key":"5962_CR13","unstructured":"Hu Q, Rangwala H (2019) Academic performance estimation with attention-based graph convolutional networks. arXiv"},{"key":"5962_CR14","doi-asserted-by":"crossref","unstructured":"Whitehill J, Mohan K, Seaton D et al (2017) Delving deeper into MOOC student dropout prediction. arXiv","DOI":"10.1145\/3051457.3053974"},{"key":"5962_CR15","doi-asserted-by":"publisher","unstructured":"Feng W, Tang J, Liu TX (2019) Understanding dropouts in MOOCs. In: 33rd AAAI conference on artificial intelligence AAAI 2019, 31st Innovative applications of artificial intelligence conference IAAI 2019, 9th AAAI symposium on educational advances in artificial intelligence EAAI 2019, vol 33, pp 517\u2013524. https:\/\/doi.org\/https:\/\/doi.org\/10.1609\/aaai.v33i01.3301517","DOI":"10.1609\/aaai.v33i01.3301517"},{"key":"5962_CR16","doi-asserted-by":"crossref","unstructured":"Fei M, Yeung DY (2016) Temporal models for predicting student dropout in massive open online courses. In: Proceedings of 15th IEEE international conference on data mining workshop, ICDMW 2015. IEEE, pp 256\u2013263","DOI":"10.1109\/ICDMW.2015.174"},{"key":"5962_CR17","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0167-7012(00)00201-3","volume":"43","author":"IA Basheer","year":"2000","unstructured":"Basheer IA, Hajmeer M (2000) Artificial neural networks: Fundamentals, computing, design, and application. J Microbiol Methods 43:3\u201331. https:\/\/doi.org\/10.1016\/S0167-7012(00)00201-3","journal-title":"J Microbiol Methods"},{"key":"5962_CR18","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.asoc.2012.07.029","volume":"13","author":"J Dai","year":"2013","unstructured":"Dai J, Xu Q (2013) Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification. Appl Soft Comput J 13:211\u2013221. https:\/\/doi.org\/10.1016\/j.asoc.2012.07.029","journal-title":"Appl Soft Comput J"},{"key":"#cr-split#-5962_CR19.1","unstructured":"Cortez P, Silva A (2008) Using data mining to predict secondary school student performance. In: 15th European concurrent engineering conference 2008, ECEC 2008"},{"key":"#cr-split#-5962_CR19.2","unstructured":"5th Future of business technology conference, FUBUTEC 2008, pp 5-12"},{"key":"5962_CR20","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s10509-013-1756-z","volume":"350","author":"EI Abouelmagd","year":"2014","unstructured":"Abouelmagd EI, Awad ME, Elzayat EMA, Abbas IA (2014) Reduction the secular solution to periodic solution in the generalized restricted three-body problem. Astrophys Space Sci 350:495\u2013505. https:\/\/doi.org\/10.1007\/s10509-013-1756-z","journal-title":"Astrophys Space Sci"},{"key":"5962_CR21","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.neucom.2015.11.046","volume":"177","author":"Y Liu","year":"2016","unstructured":"Liu Y, Liu W, Obaid MA, Abbas IA (2016) Exponential stability of Markovian jumping Cohen-Grossberg neural networks with mixed mode-dependent time-delays. Neurocomputing 177:409\u2013415. https:\/\/doi.org\/10.1016\/j.neucom.2015.11.046","journal-title":"Neurocomputing"},{"key":"5962_CR22","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.jfranklin.2015.11.013","volume":"353","author":"B Du","year":"2016","unstructured":"Du B, Liu Y, Atiatallah Abbas I (2016) Existence and asymptotic behavior results of periodic solution for discrete-time neutral-type neural networks. J Frankl Inst 353:448\u2013461. https:\/\/doi.org\/10.1016\/j.jfranklin.2015.11.013","journal-title":"J Frankl Inst"},{"key":"5962_CR23","doi-asserted-by":"publisher","first-page":"562","DOI":"10.2307\/3621816","volume":"85","author":"LG Chambers","year":"2001","unstructured":"Chambers LG, Fletcher R (2001) Practical methods of optimization. Math Gaz 85:562. https:\/\/doi.org\/10.2307\/3621816","journal-title":"Math Gaz"},{"key":"5962_CR24","doi-asserted-by":"publisher","first-page":"2013","DOI":"10.1109\/TPAMI.2011.44","volume":"33","author":"X He","year":"2011","unstructured":"He X, Ji M, Zhang C, Bao H (2011) A variance minimization criterion to feature selection using Laplacian regularization. IEEE Trans Pattern Anal Mach Intell 33:2013\u20132025. https:\/\/doi.org\/10.1109\/TPAMI.2011.44","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"5962_CR25","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1007\/s10994-013-5373-4","volume":"92","author":"Z Zhao","year":"2013","unstructured":"Zhao Z, Zhang R, Cox J et al (2013) Massively parallel feature selection: an approach based on variance preservation. Mach Learn 92:195\u2013220. https:\/\/doi.org\/10.1007\/s10994-013-5373-4","journal-title":"Mach Learn"},{"key":"5962_CR26","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1080\/03772063.2015.1021385","volume":"61","author":"C Jin","year":"2015","unstructured":"Jin C, Ma T, Hou R et al (2015) Chi-square statistics feature selection based on term frequency and distribution for text categorization. IETE J Res 61:351\u2013362. https:\/\/doi.org\/10.1080\/03772063.2015.1021385","journal-title":"IETE J Res"},{"key":"5962_CR27","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.ipm.2004.08.006","volume":"42","author":"C Lee","year":"2006","unstructured":"Lee C, Lee GG (2006) Information gain and divergence-based feature selection for machine learning-based text categorization. Inf Process Manag 42:155\u2013165. https:\/\/doi.org\/10.1016\/j.ipm.2004.08.006","journal-title":"Inf Process Manag"},{"key":"5962_CR28","doi-asserted-by":"publisher","first-page":"1024","DOI":"10.1016\/j.knosys.2011.04.014","volume":"24","author":"H U\u01e7uz","year":"2011","unstructured":"U\u01e7uz H (2011) A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Knowl Based Syst 24:1024\u20131032. https:\/\/doi.org\/10.1016\/j.knosys.2011.04.014","journal-title":"Knowl Based Syst"},{"key":"5962_CR29","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1109\/21.97458","volume":"21","author":"SR Safavian","year":"1991","unstructured":"Safavian SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21:660\u2013674. https:\/\/doi.org\/10.1109\/21.97458","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"5962_CR30","doi-asserted-by":"publisher","first-page":"107521","DOI":"10.1016\/j.patcog.2020.107521","volume":"107","author":"F Wang","year":"2020","unstructured":"Wang F, Wang Q, Nie F et al (2020) A linear multivariate binary decision tree classifier based on K-means splitting. Pattern Recognit 107:107521. https:\/\/doi.org\/10.1016\/j.patcog.2020.107521","journal-title":"Pattern Recognit"},{"key":"5962_CR31","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1109\/TSMC.1985.6313426","volume":"15","author":"JM Keller","year":"1985","unstructured":"Keller JM, Gray MR (1985) A fuzzy K-nearest neighbor algorithm. IEEE Trans Syst Man Cybern SMC 15:580\u2013585. https:\/\/doi.org\/10.1109\/TSMC.1985.6313426","journal-title":"IEEE Trans Syst Man Cybern SMC"},{"key":"5962_CR32","doi-asserted-by":"publisher","first-page":"804","DOI":"10.1109\/21.376493","volume":"25","author":"T Denoeux","year":"1995","unstructured":"Denoeux T (1995) A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans Syst Man Cybern 25:804\u2013813. https:\/\/doi.org\/10.1109\/21.376493","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"5962_CR33","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton G, Deng L, Yu D et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29:82\u201397. https:\/\/doi.org\/10.1109\/MSP.2012.2205597","journal-title":"IEEE Signal Process Mag"},{"key":"5962_CR34","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding RMSE in the literature. Geosci Model Dev 7:1247\u20131250. https:\/\/doi.org\/10.5194\/gmd-7-1247-2014","journal-title":"Geosci Model Dev"},{"key":"5962_CR35","doi-asserted-by":"crossref","unstructured":"Swamidass PM (2000) Mean absolute percentage error (MAPE). In: Encyclopedia of production and manufacturing management. Springer, Boston, pp 462\u2013462","DOI":"10.1007\/1-4020-0612-8_580"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05962-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05962-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05962-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T17:07:37Z","timestamp":1683824857000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05962-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,20]]},"references-count":36,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["5962"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05962-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,20]]},"assertion":[{"value":"8 September 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}