{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T02:50:04Z","timestamp":1769827804452,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":24,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,5]],"date-time":"2021-08-05T00:00:00Z","timestamp":1628121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001409","name":"Department of Science and Technology, Ministry of Science and Technology, India","doi-asserted-by":"publisher","award":["T-319"],"award-info":[{"award-number":["T-319"]}],"id":[{"id":"10.13039\/501100001409","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,5]]},"DOI":"10.1145\/3474124.3474164","type":"proceedings-article","created":{"date-parts":[[2021,11,4]],"date-time":"2021-11-04T22:40:25Z","timestamp":1636065625000},"page":"278-285","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Investigate the Impact of Resampling Techniques on Imbalanced Datasets: A Case Study in Plant Disease Prediction"],"prefix":"10.1145","author":[{"given":"Anshul","family":"Bhatia","sequence":"first","affiliation":[{"name":"USIC&T, GGSIP University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anuradha","family":"Chug","sequence":"additional","affiliation":[{"name":"USIC&T, GGSIP University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit","family":"Prakash Singh","sequence":"additional","affiliation":[{"name":"USIC&T, GGSIP University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dinesh","family":"Singh","sequence":"additional","affiliation":[{"name":"Indian Agricultural Research Institute, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,11,4]]},"reference":[{"key":"e_1_3_2_1_1_1","first-page":"2","article-title":"2013. Validation of Tomato Powdery Mildew Forecasting Model using Meteorological Data","volume":"5","author":"Bakeer A.R.T.","year":"2013","unstructured":"Bakeer , A.R.T. , Abdel-Latef , M.A.E. , Afifi , M.A. and Barakat , M.E . 2013. Validation of Tomato Powdery Mildew Forecasting Model using Meteorological Data in Egypt. International Journal of Agriculture Sciences. 5 , 2 ( 2013 ), 372. Bakeer, A.R.T., Abdel-Latef, M.A.E., Afifi, M.A. and Barakat, M.E. 2013. Validation of Tomato Powdery Mildew Forecasting Model using Meteorological Data in Egypt. International Journal of Agriculture Sciences. 5, 2 (2013), 372.","journal-title":"Egypt. International Journal of Agriculture Sciences."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"crossref","unstructured":"Batista G.E. Prati R.C. and Monard M.C. 2004. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter. 6 1 (2004) 20\u201329.  Batista G.E. Prati R.C. and Monard M.C. 2004. A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD explorations newsletter. 6 1 (2004) 20\u201329.","DOI":"10.1145\/1007730.1007735"},{"key":"e_1_3_2_1_3_1","volume-title":"Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. Journal of Statistics and Management Systems. 23, 6","author":"Bhatia A.","year":"2020","unstructured":"Bhatia , A. , Chug , A. and Prakash Singh , A. 2020. Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. Journal of Statistics and Management Systems. 23, 6 ( 2020 ), 1059\u20131068. Bhatia, A., Chug, A. and Prakash Singh, A. 2020. Application of extreme learning machine in plant disease prediction for highly imbalanced dataset. Journal of Statistics and Management Systems. 23, 6 (2020), 1059\u20131068."},{"key":"e_1_3_2_1_4_1","volume-title":"Hybrid SVM-LR Classifier for Powdery Mildew Disease Prediction in Tomato Plant. 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN)","author":"Bhatia A.","year":"2020","unstructured":"Bhatia , A. , Chug , A. and Singh , A.P . 2020 . Hybrid SVM-LR Classifier for Powdery Mildew Disease Prediction in Tomato Plant. 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) ( 2020 ), 218\u2013223. Bhatia, A., Chug, A. and Singh, A.P. 2020. Hybrid SVM-LR Classifier for Powdery Mildew Disease Prediction in Tomato Plant. 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN) (2020), 218\u2013223."},{"key":"e_1_3_2_1_5_1","first-page":"4","article-title":"2020. Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach","volume":"13","author":"Bhatia A.","year":"2020","unstructured":"Bhatia , A. , Chug , A. and Singh , A.P . 2020. Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach . International Journal of Future Generation Communication and Networking. 13 , 4 ( 2020 ), 71\u201378. Bhatia, A., Chug, A. and Singh, A.P. 2020. Plant disease detection for high dimensional imbalanced dataset using an enhanced decision tree approach. International Journal of Future Generation Communication and Networking. 13, 4 (2020), 71\u201378.","journal-title":"International Journal of Future Generation Communication and Networking."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1504\/IJIEI.2021.116087"},{"key":"e_1_3_2_1_7_1","unstructured":"Branco P. Ribeiro R.P. and Torgo L. 2016. UBL: an R package for utility-based learning. arXiv preprint arXiv:1604.08079. (2016).  Branco P. Ribeiro R.P. and Torgo L. 2016. UBL: an R package for utility-based learning. arXiv preprint arXiv:1604.08079. (2016)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Chawla N. V Bowyer K.W. Hall L.O. and Kegelmeyer W.P. 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research. 16 (2002) 321\u2013357.  Chawla N. V Bowyer K.W. Hall L.O. and Kegelmeyer W.P. 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research. 16 (2002) 321\u2013357.","DOI":"10.1613\/jair.953"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Fern\u00e1Ndez A. L\u00f3Pez V. Galar M. Del Jesus M.J. and Herrera F. 2013. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-based systems. 42 (2013) 97\u2013110.  Fern\u00e1Ndez A. L\u00f3Pez V. Galar M. Del Jesus M.J. and Herrera F. 2013. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches. Knowledge-based systems. 42 (2013) 97\u2013110.","DOI":"10.1016\/j.knosys.2013.01.018"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2017.01.005"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Kang Q. Chen X. Li S. and Zhou M. 2016. A noise-filtered under-sampling scheme for imbalanced classification. IEEE transactions on cybernetics. 47 12 (2016) 4263\u20134274.  Kang Q. Chen X. Li S. and Zhou M. 2016. A noise-filtered under-sampling scheme for imbalanced classification. IEEE transactions on cybernetics. 47 12 (2016) 4263\u20134274.","DOI":"10.1109\/TCYB.2016.2606104"},{"key":"e_1_3_2_1_12_1","volume-title":"Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence. 5, 4","author":"Krawczyk B.","year":"2016","unstructured":"Krawczyk , B. 2016. Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence. 5, 4 ( 2016 ), 221\u2013232. Krawczyk, B. 2016. Learning from imbalanced data: open challenges and future directions. Progress in Artificial Intelligence. 5, 4 (2016), 221\u2013232."},{"key":"e_1_3_2_1_13_1","volume-title":"Machine learning with R","author":"Lantz B.","unstructured":"Lantz , B. 2013. Machine learning with R . Packt Publishing Ltd . Lantz, B. 2013. Machine learning with R. Packt Publishing Ltd."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1080\/01431160412331269698"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/IMIS.2014.6"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigDataService.2017.19"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.6000\/1927-5129.2017.13.76"},{"key":"e_1_3_2_1_18_1","unstructured":"Starkweather J. and Moske A.K. 2011. Multinomial logistic regression. Consulted page at September 10th: http:\/\/www.unt.edu\/rss\/class\/Jon\/Benchmarks\/MLR_JDS_Aug2011.pdf. 29 (2011) 2825\u20132830.  Starkweather J. and Moske A.K. 2011. Multinomial logistic regression. Consulted page at September 10th: http:\/\/www.unt.edu\/rss\/class\/Jon\/Benchmarks\/MLR_JDS_Aug2011.pdf. 29 (2011) 2825\u20132830."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Sutton C.D. 2005. Classification and regression trees bagging and boosting. Handbook of statistics. 24 (2005) 303\u2013329.  Sutton C.D. 2005. Classification and regression trees bagging and boosting. Handbook of statistics. 24 (2005) 303\u2013329.","DOI":"10.1016\/S0169-7161(04)24011-1"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2016.7743853"},{"key":"e_1_3_2_1_21_1","volume-title":"MRPR: A MapReduce solution for prototype reduction in big data classification. neurocomputing. 150","author":"Triguero I.","year":"2015","unstructured":"Triguero , I. , Peralta , D. , Bacardit , J. , Garc\u00eda , S. and Herrera , F . 2015 . MRPR: A MapReduce solution for prototype reduction in big data classification. neurocomputing. 150 , (2015), 331\u2013345. Triguero, I., Peralta, D., Bacardit, J., Garc\u00eda, S. and Herrera, F. 2015. MRPR: A MapReduce solution for prototype reduction in big data classification. neurocomputing. 150, (2015), 331\u2013345."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2015.05.027"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Troncoso A. Ribera P. Asencio-Cort\u00e9s G. Vega I. and Gallego D. 2018. Imbalanced classification techniques for monsoon forecasting based on a new climatic time series. Environmental modelling & software. 106 (2018) 48\u201356.  Troncoso A. Ribera P. Asencio-Cort\u00e9s G. Vega I. and Gallego D. 2018. Imbalanced classification techniques for monsoon forecasting based on a new climatic time series. Environmental modelling & software. 106 (2018) 48\u201356.","DOI":"10.1016\/j.envsoft.2017.11.024"},{"key":"e_1_3_2_1_24_1","volume-title":"A comparative study for email classification. Advances and innovations in systems, computing sciences and software engineering","author":"Youn S.","unstructured":"Youn , S. and McLeod , D. 2007. A comparative study for email classification. Advances and innovations in systems, computing sciences and software engineering . Springer . 387\u2013391. Youn, S. and McLeod, D. 2007. A comparative study for email classification. Advances and innovations in systems, computing sciences and software engineering. Springer. 387\u2013391."}],"event":{"name":"IC3 '21: 2021 Thirteenth International Conference on Contemporary Computing","location":"Noida India","acronym":"IC3 '21"},"container-title":["2021 Thirteenth International Conference on Contemporary Computing (IC3-2021)"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474124.3474164","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3474124.3474164","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:17:36Z","timestamp":1750191456000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3474124.3474164"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,5]]},"references-count":24,"alternative-id":["10.1145\/3474124.3474164","10.1145\/3474124"],"URL":"https:\/\/doi.org\/10.1145\/3474124.3474164","relation":{},"subject":[],"published":{"date-parts":[[2021,8,5]]},"assertion":[{"value":"2021-11-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}