{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T01:23:20Z","timestamp":1768440200661,"version":"3.49.0"},"reference-count":27,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,5]]},"DOI":"10.1109\/ijcnn.2017.7965999","type":"proceedings-article","created":{"date-parts":[[2017,7,10]],"date-time":"2017-07-10T17:41:30Z","timestamp":1499708490000},"page":"1273-1280","source":"Crossref","is-referenced-by-count":11,"title":["Classifying commit messages: A case study in resampling techniques"],"prefix":"10.1109","author":[{"given":"SeyedHamid","family":"Shekarforoush","sequence":"first","affiliation":[]},{"given":"Robert","family":"Green","sequence":"additional","affiliation":[]},{"given":"Robert","family":"Dyer","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5422-z"},{"key":"ref11","first-page":"321","article-title":"SMOTE: synthetic minority over-sampling technique","volume":"16","author":"chawla","year":"2002","journal-title":"Artif in Tell Re"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"878","DOI":"10.1007\/11538059_91","article-title":"Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning","author":"han","year":"2005","journal-title":"Advances in Intelligent Computing"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1504\/IJKESDP.2011.039875"},{"key":"ref14","article-title":"ADASYN: Adaptive synthetic sampling approach for imbalanced learning","author":"he","year":"2008","journal-title":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)"},{"key":"ref15","first-page":"10","article-title":"Balancing Training Data for Automated Annotation of Keywords: a Case Study","author":"batista","year":"2003","journal-title":"WOB"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1145\/1007730.1007735"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/16.5.412"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.08.025"},{"key":"ref19","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref4","author":"lemaitre","year":"2016","journal-title":"Imbalanced-learn A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning"},{"key":"ref27","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TSMCB.2008.2007853","article-title":"Exploratory undersampling for class-imbalance learning","volume":"39","author":"liu","year":"2009","journal-title":"IEEE Trans Syst Man Cybern B Cybern"},{"key":"ref3","first-page":"769","article-title":"Two Modifications of CNN","volume":"6","author":"tomek","year":"1976","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"ref6","first-page":"179","article-title":"Addressing the curse of imbalanced training sets: one-sided selection","volume":"97","author":"kubat","year":"1997","journal-title":"ICML"},{"key":"ref5","article-title":"kNN approach to unbalanced data distributions: a case study involving information extraction","author":"mani","year":"2003","journal-title":"Proceedings of Workshop on Learning from Imbalanced Datasets"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.1972.4309137"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/3-540-48229-6_9","article-title":"Improving Identification of Difficult Small Classes by Balancing Class Distribution","author":"laurikkala","year":"2001","journal-title":"Artificial Intelligence in Medicine"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054155"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TSMC.1976.4309523","article-title":"An experiment with the edited nearest-neighbor rule","volume":"smc 6","author":"tomek","year":"1976","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2989238.2989244"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1023\/B:STCO.0000035301.49549.88"},{"key":"ref21","author":"breiman","year":"1984","journal-title":"Classification and Regression Trees"},{"key":"ref24","first-page":"41","article-title":"A comparison of event models for naive bayes text classification","volume":"752","author":"mccallum","year":"1998","journal-title":"AAAI-98 Workshop on Learning for Text Categorization"},{"key":"ref23","first-page":"1871","article-title":"LIBLINEAR: A Library for Large Linear Classification","volume":"9","author":"fan","year":"2008","journal-title":"J Mach Learn Res"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511809071.014"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.082099299"}],"event":{"name":"2017 International Joint Conference on Neural Networks (IJCNN)","location":"Anchorage, AK, USA","start":{"date-parts":[[2017,5,14]]},"end":{"date-parts":[[2017,5,19]]}},"container-title":["2017 International Joint Conference on Neural Networks (IJCNN)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7958416\/7965814\/07965999.pdf?arnumber=7965999","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,29]],"date-time":"2019-09-29T06:20:06Z","timestamp":1569738006000},"score":1,"resource":{"primary":{"URL":"http:\/\/ieeexplore.ieee.org\/document\/7965999\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,5]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/ijcnn.2017.7965999","relation":{},"subject":[],"published":{"date-parts":[[2017,5]]}}}