{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T05:03:26Z","timestamp":1768885406974,"version":"3.49.0"},"reference-count":74,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Liquid Intelligent Technologies South Africa"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"DOI":"10.1186\/s40537-024-01020-6","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T09:07:52Z","timestamp":1730970472000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Using topological data analysis and machine learning to predict customer churn"],"prefix":"10.1186","volume":"11","author":[{"given":"Marcel","family":"Sagming","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reolyn","family":"Heymann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria Vivien","family":"Visaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"1020_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2021.668302","volume":"4","author":"H Adams","year":"2021","unstructured":"Adams H, Moy M. Topology applied to machine learning: from global to local. Front Artif Intell. 2021;4: 668302.","journal-title":"Front Artif Intell"},{"key":"1020_CR2","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1016\/j.jbusres.2018.03.003","volume":"94","author":"A Amin","year":"2019","unstructured":"Amin A, Al-Obeidat F, Shah B, Adnan A, Loo J, Anwar S. Customer churn prediction in telecommunication industry using data certainty. J Bus Res. 2019;94:290\u2013301.","journal-title":"J Bus Res"},{"key":"1020_CR3","unstructured":"Ye A. Stop one-hot encoding your categorical variables. https:\/\/towardsdatascience.com\/stop-one-hot-encoding-your-categorical-variables-bbb0fba89809. Accessed 03 Nov 2021."},{"key":"1020_CR4","unstructured":"Valentino A. Machine learning techniques for customer churn prediction in banking environments. http:\/\/tesi.cab.unipd.it\/53212\/1\/Valentino_Avon_-_1104319.pdf. Accessed 26 Jan 2022."},{"key":"1020_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2021.100395","volume":"40","author":"T Balaji","year":"2021","unstructured":"Balaji T, Annavarapu C, Bablani A. Machine learning algorithms for social media analysis: a survey. Comp Sci Rev. 2021;40: 100395. https:\/\/doi.org\/10.1016\/j.cosrev.2021.100395.","journal-title":"Comp Sci Rev"},{"key":"1020_CR6","unstructured":"Bex T. How to use variance thresholding for robust feature selection. https:\/\/towardsdatascience.com\/how-to-use-variance-thresholding-for-robust-feature-selection-a4503f2b5c3f. Accessed 05 Nov 2021."},{"key":"1020_CR7","doi-asserted-by":"crossref","unstructured":"Bhatnagar A, Srivastava S. A robust model for churn prediction using supervised machine learning. In: Proceedings of the IEEE 9th International Conference on Advanced Computing (IACC). 2019. pp. 45-49.","DOI":"10.1109\/IACC48062.2019.8971494"},{"key":"1020_CR8","doi-asserted-by":"crossref","unstructured":"Bhuse P, Gandhi A, Meswani P, Muni R, Katre N. Machine learning based telecom-customer churn prediction. In: Proceedings of the 3rd International Conference on Intelligent Sustainable Systems (ICISS). 2020. pp. 1297\u20131301.","DOI":"10.1109\/ICISS49785.2020.9315951"},{"key":"1020_CR9","volume-title":"Pattern recognition and machine learning","author":"CM Bishop","year":"2006","unstructured":"Bishop CM. Pattern recognition and machine learning. New York: Springer; 2006."},{"key":"1020_CR10","unstructured":"Brownlee J. Why one-hot encode data in machine learning?. https:\/\/machinelearningmastery.com\/why-one-hot-encode-data-in-machine-learning. Accessed 03 Nov 2021."},{"key":"1020_CR11","unstructured":"De Silva V, Carlsson G. Topological estimation using witness complexes. In: Proceedings of the 1st Eurographics Symposium on Point-Based Graphics. 2004. pp. 157\u2013166."},{"issue":"2","key":"1020_CR12","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1090\/S0273-0979-09-01249-X","volume":"46","author":"G Carlsson","year":"2009","unstructured":"Carlsson G. Topology and data. AMS Bull. 2009;46(2):255.","journal-title":"AMS Bull"},{"key":"1020_CR13","first-page":"1","volume":"6","author":"G Chandana","year":"2018","unstructured":"Chandana S, Vineetha G, Varun E, Ravikumar P. Analysis of Telecom Customer churn prediction by building decision tree. IJERT. 2018;6:1.","journal-title":"IJERT"},{"key":"1020_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2021.667963","author":"F Chazal","year":"2021","unstructured":"Chazal F, Michel B. An introduction to topological data analysis: fundamental and practical aspects for data scientists. Front Artif Intell. 2021. https:\/\/doi.org\/10.3389\/frai.2021.667963.","journal-title":"Front Artif Intell"},{"key":"1020_CR15","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. 2016. pp. 785\u2013794.","DOI":"10.1145\/2939672.2939785"},{"key":"1020_CR16","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1007\/s00454-006-1276-5","volume":"37","author":"D Cohen-Steiner","year":"2007","unstructured":"Cohen-Steiner D, Edelsbrunner H, Harer J. Stability of persistence diagrams. Dis Comp Geo. 2007;37:103\u201320.","journal-title":"Dis Comp Geo"},{"key":"1020_CR17","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","volume":"13","author":"T Cover","year":"1967","unstructured":"Cover T, Hart P. Nearest neighbor pattern classification. IEEE Tran Inf Theo. 1967;13:21\u20137.","journal-title":"IEEE Tran Inf Theo"},{"key":"1020_CR18","doi-asserted-by":"publisher","DOI":"10.1080\/23746149.2023.2202331","author":"D Leykam","year":"2023","unstructured":"Leykam D, Angelakis DG. Topological data analysis and machine learning. Adv Phys. 2023. https:\/\/doi.org\/10.1080\/23746149.2023.2202331.","journal-title":"Adv Phys"},{"key":"1020_CR19","first-page":"P03034","volume":"3","author":"D Horak","year":"2009","unstructured":"Horak D, Melatic S, Rajkovic M. Persistent homology of complex networks. IOP. 2009;3:P03034.","journal-title":"IOP"},{"key":"1020_CR20","doi-asserted-by":"publisher","first-page":"e1195","DOI":"10.7717\/peerj-cs.1195","volume":"9","author":"MLD De Lara","year":"2023","unstructured":"De Lara MLD. Persistent homology classification algorithm. PeerJ Comput Sci. 2023;9:e1195.","journal-title":"PeerJ Comput Sci"},{"key":"1020_CR21","unstructured":"Dogan Y. KDD Cup 2009 data analysis. https:\/\/www.kaggle.com\/code\/yasmino\/kdd-cup-2009-data-analysis\/data. Accessed 03 Nov 2021."},{"key":"1020_CR22","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2021.681108","author":"F Hensel","year":"2021","unstructured":"Hensel F, Moor M, Rieck B. A survey of topological machine learning methods. Front Artif Intell. 2021. https:\/\/doi.org\/10.3389\/frai.2021.681108.","journal-title":"Front Artif Intell"},{"key":"1020_CR23","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s42979-023-02396-7","volume":"5","author":"M Flammer","year":"2024","unstructured":"Flammer M. Persistent homology-based classification of chaotic multi-variate time series: application to electroencephalograms. SN Comput Sci. 2024;5:107.","journal-title":"SN Comput Sci"},{"key":"1020_CR24","unstructured":"Benram G. XGBoost or TensorFlow?. https:\/\/www.doit-intl.com\/xgboost-or-tensorflow\/. Accessed 24 Jan 2022."},{"key":"1020_CR25","first-page":"49","volume":"334","author":"J Garland","year":"2016","unstructured":"Garland J, Bradley E, Meiss JD. Exploring the topology of dynamical reconstructions. Phy Non Phen. 2016;334:49\u201359.","journal-title":"Phy Non Phen"},{"key":"1020_CR26","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1090\/S0273-0979-07-01191-3","volume":"45","author":"R Ghrist","year":"2008","unstructured":"Ghrist R. Barcodes: the persistent topology of data. Bull Am Math Soc. 2008;45:61\u201375.","journal-title":"Bull Am Math Soc"},{"key":"1020_CR27","unstructured":"Malato G. Feature selection in machine learning using Lasso regression. https:\/\/towardsdatascience.com\/feature-selection-in-machine-learning-using-Lasso-regression-7809c7c2771a. Accessed 05 Nov 2021."},{"key":"1020_CR28","doi-asserted-by":"crossref","unstructured":"Giansiracusa N, Giansiracusa R, Moon C. Persistent homology machine learning for fingerprint classification. In: Proceedings of the 18th IEEE International Conference On Machine Learning And Applications (ICMLA). 2019. pp. 1219\u20131226.","DOI":"10.1109\/ICMLA.2019.00201"},{"key":"1020_CR29","doi-asserted-by":"publisher","first-page":"181","DOI":"10.9734\/jerr\/2024\/v26i21081","volume":"26","author":"G Sam","year":"2024","unstructured":"Sam G, Asuquo P, Stephen B. Customer churn prediction using machine learning models. J Eng Res Rep. 2024;26:181\u201393.","journal-title":"J Eng Res Rep"},{"key":"1020_CR30","doi-asserted-by":"publisher","DOI":"10.1080\/00051144.2010.11828381","author":"G Kraljevic","year":"2010","unstructured":"Kraljevic G, Gotovac S. Modeling data mining applications for prediction of prepaid churn in telecommunication services. Automatika. 2010. https:\/\/doi.org\/10.1080\/00051144.2010.11828381.","journal-title":"Automatika"},{"key":"1020_CR31","doi-asserted-by":"crossref","unstructured":"Hassonah MA, Rodan A, Al-Tamimi A, Alsakran J. Churn prediction: a comparative study using knn and decision trees. In: Proceedings of the 6th HCT Information Technology Trends (ITT). 2019. pp. 182\u2013186.","DOI":"10.1109\/ITT48889.2019.9075077"},{"key":"1020_CR32","first-page":"751","volume":"28","author":"HS Kim","year":"2004","unstructured":"Kim HS, Yoon CH. Determinants of subscriber churn and customer loyalty in the Korean mobile telephony market. Tel Pol. 2004;28:751\u201365.","journal-title":"Tel Pol"},{"key":"1020_CR33","unstructured":"Adams H, Tausz A. Machine learning techniques for customer churn prediction in banking environments. https:\/\/www.math.colostate.edu\/~adams\/research\/javaplex_tutorial.pdf. Accessed 15 Jan 2022."},{"issue":"1","key":"1020_CR34","first-page":"2202331","volume":"8","author":"D Leykam","year":"2023","unstructured":"Leykam D, Angelakis DG. Topological data analysis and machine learning. Adv Phys X. 2023;8(1):2202331.","journal-title":"Adv Phys X"},{"key":"1020_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102706","volume":"58","author":"I Pustokhina","year":"2021","unstructured":"Pustokhina\u00a0I,\u00a0Pustokhin\u00a0D, Aswathy RH,\u00a0Thangaiyan\u00a0T, et al. Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimisation algorithms. Inf Proc Man. 2021;58: 102706.","journal-title":"Inf Proc Man"},{"key":"1020_CR36","unstructured":"Brownlee J. A Gentle Introduction to XGBoost for Applied Machine Learning. https:\/\/machinelearningmastery.com\/gentle-introduction-xgboost-applied-machine-learning\/. Accessed 24 Jan 2022."},{"key":"1020_CR37","doi-asserted-by":"publisher","first-page":"2991","DOI":"10.48084\/etasr.2108","volume":"8","author":"E Jamalian","year":"2018","unstructured":"Jamalian E, Foukerdi R. A hybrid data mining method for customer churn prediction. Eng Tech Appl Sci Res. 2018;8:2991\u20137.","journal-title":"Eng Tech Appl Sci Res"},{"key":"1020_CR38","doi-asserted-by":"publisher","first-page":"2902","DOI":"10.1016\/j.cor.2005.11.007","volume":"34","author":"J Hadden","year":"2007","unstructured":"Hadden J, Tiwari A, Roy R, Ruta D. Computer assisted customer churn management: state-of-the-art and future trends. Comp Op Res. 2007;34:2902\u201317.","journal-title":"Comp Op Res"},{"key":"1020_CR39","first-page":"306","volume":"1","author":"C Kang","year":"2008","unstructured":"Kang C, Pei-j S. Customer churn prediction based on SVM-RFE. Intl Sem Bus Inf Man. 2008;1:306\u20139.","journal-title":"Intl Sem Bus Inf Man"},{"key":"1020_CR40","first-page":"2378877","volume":"10","author":"KC Mouli","year":"2024","unstructured":"Mouli KC, Raghavendranb C, Bharadwaja V, Vybhavia G, Sravania C, Khristina M, Rajesh D, Laith H. An analysis on classification models for customer churn prediction. Co Eng. 2024;10:2378877.","journal-title":"Co Eng"},{"key":"1020_CR41","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.jbusres.2006.05.011","volume":"61","author":"E Ko","year":"2008","unstructured":"Ko E, Kim SH, Kim M, Woo JY. Organisational characteristics and the CRM adoption process. J Bus Res. 2008;61:65\u201374.","journal-title":"J Bus Res"},{"key":"1020_CR42","unstructured":"Chakraborty K. Customer Relationship Prediction: KDD Cup 2009. https:\/\/medium.com\/@kushaldps1996\/customer-relationship-prediction-KDD-cup-2009-6b57d08ffb0. Accessed 03 2021."},{"key":"1020_CR43","first-page":"34","volume":"33","author":"V Lazarov","year":"2007","unstructured":"Lazarov V, Capota M. Churn prediction. Bus Anal Course TUM Comput Sci. 2007;33:34.","journal-title":"Bus Anal Course TUM Comput Sci"},{"key":"1020_CR44","doi-asserted-by":"publisher","DOI":"10.1063\/5.0159349","author":"E Minamitani","year":"2023","unstructured":"Minamitani E, Obayashi I. Persistent homology-based descriptor for machine-learning potential. Phys J Chem. 2023. https:\/\/doi.org\/10.1063\/5.0159349.","journal-title":"Phys J Chem"},{"key":"1020_CR45","unstructured":"Mitchell R, Adinets A, Rao T, Frank E. Xgboost: Scalable GPU accelerated learning. 2018. arXiv, arXiv:1806.11248."},{"key":"1020_CR46","doi-asserted-by":"publisher","DOI":"10.1088\/1475-7516\/2016\/12\/008","author":"A Moller","year":"2016","unstructured":"Moller A, Ruhlmann-Kleider V, Leloup C, Neveu J, Palanque-Delabrouille N, Rich J, Carlberg R, Lidman C, Pritchet C. Photometric classification of type la supernovae in the SuperNova Legacy Survey with supervised learning. J Cos Astro Phy. 2016. https:\/\/doi.org\/10.1088\/1475-7516\/2016\/12\/008.","journal-title":"J Cos Astro Phy"},{"key":"1020_CR47","volume-title":"Elements of algebraic topology","author":"JR Munkres","year":"1984","unstructured":"Munkres JR. Elements of algebraic topology. Redwood city: Addison Westley; 1984."},{"key":"1020_CR48","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1002\/rsa.20697","volume":"51","author":"O Bobrowski","year":"2016","unstructured":"Bobrowski O,\u00a0Weinberger\u00a0S. On the vanishing of homology in random \u010cech Complexes. Ran Struct Alg. 2016;51:14\u201351.","journal-title":"Ran Struct Alg"},{"key":"1020_CR49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1140\/epjds\/s13688-016-0097-x","volume":"6","author":"N Otter","year":"2017","unstructured":"Otter N, Porter MA, Tillmann U, et al. A roadmap for the computation of persistent homology. EPJ D Sci. 2017;6:1\u201338.","journal-title":"EPJ D Sci"},{"key":"1020_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.119356","volume":"256","author":"L Pei","year":"2020","unstructured":"Pei L, Sun Z, Yu T, Li W, Hao X, Hu Y, Yang C. Pavement aggregate shape classification based on extreme gradient boosting. Const Buil Mat. 2020;256: 119356.","journal-title":"Const Buil Mat"},{"key":"1020_CR51","first-page":"145","volume":"4","author":"B Prabadevi","year":"2023","unstructured":"Prabadevi B, Shalini R, Kavitha B. Customer churning analysis using machine learning algorithms. Int J Intel Net. 2023;4:145\u201354.","journal-title":"Int J Intel Net"},{"key":"1020_CR52","first-page":"1149","volume":"119","author":"P Asthana","year":"2017","unstructured":"Asthana P. A comparison of machine learning techniques for customer churn prediction. Intl J Pu Appl Mat. 2017;119:1149\u201369.","journal-title":"Intl J Pu Appl Mat"},{"key":"1020_CR53","doi-asserted-by":"publisher","first-page":"5169","DOI":"10.1007\/s10462-022-10146-z","volume":"55","author":"CS Pun","year":"2022","unstructured":"Pun CS, Xia K, Lee S. Persistent-homology-based machine learning: a survey and a comparative study. Artif Intell Rev. 2022;55:5169\u2013213.","journal-title":"Artif Intell Rev"},{"key":"1020_CR54","first-page":"3961","volume":"17","author":"A Rodan","year":"2014","unstructured":"Rodan A, Faris H, Alsakran J, Al-Kadi O. A support vector machine approach for churn prediction in telecom industry. Intl J Inf. 2014;17:3961\u201370.","journal-title":"Intl J Inf"},{"key":"1020_CR55","first-page":"1","volume-title":"Churn prediction in telecommunication industry using decision tree","author":"MN Saini","year":"2017","unstructured":"Saini MN, Monika GK, Garg K. Churn prediction in telecommunication industry using decision tree. Inf. Oc.: Str; 2017. p. 1."},{"key":"1020_CR56","first-page":"2542","volume":"12","author":"M Seifeddine","year":"2020","unstructured":"Seifeddine M, Bradai A, Bukhari SH, Anh QP, Atri M, Ahmed O. Survey on machine learning in internet of things: algorithms, strategies, and applications. IoT. 2020;12:2542\u20136605.","journal-title":"IoT"},{"key":"1020_CR57","first-page":"693","volume":"2","author":"E Shaaban","year":"2012","unstructured":"Shaaban E, Helmy Y, Khedr A, Nasr M. A proposed churn prediction model. Int J Eng Res App. 2012;2:693\u20137.","journal-title":"Int J Eng Res App"},{"key":"1020_CR58","unstructured":"Shipra S. Here\u2019s All you Need to Know About Encoding Categorical Data (with Python code). https:\/\/www.analyticsvidhya.com\/blog\/2020\/08\/types-of-categorical-data-encoding. Accessed 03 Nov 2021."},{"key":"1020_CR59","doi-asserted-by":"crossref","unstructured":"Singh M, Singh S, Seen N, Kaushal S, Kumar H. Comparison of learning techniques for prediction of customer churn in telecommunication. In: Proceedings of the IEEE 28th International Telecommunication Networks and Applications Conference (ITNAC). 2018. pp. 1\u20135.","DOI":"10.1109\/ATNAC.2018.8615326"},{"key":"1020_CR60","first-page":"253","volume":"42","author":"P \u0160kraba","year":"2018","unstructured":"\u0160kraba P. Persistent homology and machine learning. Informatica. 2018;42:253\u20138.","journal-title":"Informatica"},{"key":"1020_CR61","doi-asserted-by":"publisher","DOI":"10.3847\/2041-8205\/832\/2\/L22","author":"D Tamayo","year":"2016","unstructured":"Tamayo D, Silburt A, Valencia D, Menou K, Ali-Dib M, Petrovich C, Huang C, Rein H, Laerhoven C, Paradise A, Obertas A, Murray NA. Machine learns to predict the stability of tightly packed planetary systems. Astro J. 2016. https:\/\/doi.org\/10.3847\/2041-8205\/832\/2\/L22.","journal-title":"Astro J"},{"key":"1020_CR62","doi-asserted-by":"crossref","unstructured":"Tang Q, Xia G, Zhang X, Long F. A customer churn prediction model based on XGBoost and MLP. In: Proceedings of the International Conference on Computer Engineering and Application (ICCEA). 2020. pp. 608\u2013612.","DOI":"10.1109\/ICCEA50009.2020.00133"},{"key":"1020_CR63","doi-asserted-by":"crossref","unstructured":"Tang P. Telecom customer churn prediction model combining K-means and XGBoost Algorithm. In: Proceedings of the 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE). 2020. pp. 1128\u20131131.","DOI":"10.1109\/ICMCCE51767.2020.00248"},{"key":"1020_CR64","unstructured":"Thirumuruganathan S. Detailed Introduction to K-Nearest Neighbor (KNN) Algorithm. https:\/\/saravananthirumuruganathan.wordpress.com\/2010\/05\/17\/a-detailed-introduction-to-k-nearest-neighbor-knn-algorithm\/. Accessed on 24 Jan 2022."},{"key":"1020_CR65","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/S0377-2217(03)00069-9","volume":"157","author":"D Van den Poel","year":"2004","unstructured":"Van den Poel D, Lariviere B. Customer attrition analysis for financial services using proportional hazard models. Eu J Op Res. 2004;157:196\u2013217.","journal-title":"Eu J Op Res"},{"key":"1020_CR66","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1109\/72.788640","volume":"10","author":"VN Vapnik","year":"1999","unstructured":"Vapnik VN. An overview of statistical learning theory. IEEE Trans Neu Net. 1999;10:988\u201399.","journal-title":"IEEE Trans Neu Net"},{"key":"1020_CR67","first-page":"231","volume":"17","author":"V Chang","year":"2024","unstructured":"Chang V, Hall K, Xu Q, Ganatra M, Amao FO, Benson V. Prediction of customer churn behavior in the telecommunication industry using machine learning models. Al MDPI. 2024;17:231.","journal-title":"Al MDPI"},{"key":"1020_CR68","unstructured":"Wang KG. The basic theory of persistent homology. https:\/\/math.uchicago.edu\/~may\/REU2012\/REUPapers\/WangK.pdf. Accessed 15 Jan 2022."},{"key":"1020_CR69","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/S0925-2312(02)00632-X","volume":"55","author":"W Wang","year":"2003","unstructured":"Wang W, Xu Z, Lu WZ, Zhang X. Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing. 2003;55:643\u201363.","journal-title":"Neurocomputing"},{"key":"1020_CR70","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1016\/j.neucom.2020.07.061","volume":"415","author":"L Yang","year":"2020","unstructured":"Yang L, Shami A. On hyperparameter optimisation of machine learning algorithms: theory and practice. Neurocomputing. 2020;415:295\u2013316.","journal-title":"Neurocomputing"},{"key":"1020_CR71","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1016\/j.jbusres.2021.01.022","volume":"131","author":"Y Li","year":"2021","unstructured":"Li Y, Hou B, Wu Y, Zhao D, Xie A, Zou P. Giant fight: customer churn prediction in traditional broadcast industry. J Bus Res. 2021;131:630\u20139.","journal-title":"J Bus Res"},{"key":"1020_CR72","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1023\/A:1015244902967","volume":"15","author":"K Yu","year":"2002","unstructured":"Yu K, Ji L, Zhang X. Kernel nearest-neighbor algorithm. Neu Proc Let. 2002;15:147\u201356.","journal-title":"Neu Proc Let"},{"key":"1020_CR73","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-024-10710-9","author":"A Zia","year":"2024","unstructured":"Zia A, Khamis A, Nichols J, et al. Topological deep learning: a review of an emerging paradigm. Artif Intell Rev. 2024. https:\/\/doi.org\/10.1007\/s10462-024-10710-9.","journal-title":"Artif Intell Rev"},{"key":"1020_CR74","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s10044-007-0100-z","volume":"11","author":"W Zuo","year":"2008","unstructured":"Zuo W, Zhang D, Wang K. On kernel difference-weighted k-nearest neighbor classification. Pat Ana App. 2008;11:247\u201357.","journal-title":"Pat Ana App"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-01020-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-024-01020-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-024-01020-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T10:03:52Z","timestamp":1730973832000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-024-01020-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"references-count":74,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["1020"],"URL":"https:\/\/doi.org\/10.1186\/s40537-024-01020-6","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,7]]},"assertion":[{"value":"4 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"160"}}