{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T20:03:44Z","timestamp":1775246624654,"version":"3.50.1"},"reference-count":55,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T00:00:00Z","timestamp":1539043200000},"content-version":"vor","delay-in-days":281,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Complexity"],"published-print":{"date-parts":[[2018,1]]},"abstract":"<jats:p>The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification\/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning\u2010based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide a mapping of words with similar meaning to have similar representation. In this study, we use different document representations with the benefit of word embeddings and an ensemble of base classifiers for text classification. The ensemble of base classifiers includes traditional machine learning algorithms such as na\u00efve Bayes, support vector machine, and random forest and a deep learning\u2010based conventional network classifier. We analysed the classification accuracy of different document representations by employing an ensemble of classifiers on eight different datasets. Experimental results demonstrate that the usage of heterogeneous ensembles together with deep learning methods and word embeddings enhances the classification performance of texts.<\/jats:p>","DOI":"10.1155\/2018\/7130146","type":"journal-article","created":{"date-parts":[[2018,10,9]],"date-time":"2018-10-09T19:37:09Z","timestamp":1539113829000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Deep Learning\u2010 and Word Embedding\u2010Based Heterogeneous Classifier Ensembles for Text Classification"],"prefix":"10.1155","volume":"2018","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1497-305X","authenticated-orcid":false,"given":"Zeynep H.","family":"Kilimci","sequence":"first","affiliation":[]},{"given":"Selim","family":"Akyokus","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2018,10,9]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1145\/505282.505283"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-3223-4_6"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-009-9124-7"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCAS.2006.1688199"},{"key":"e_1_2_9_5_2","volume-title":"Department of Computer Science","author":"Reid S.","year":"2007"},{"key":"e_1_2_9_6_2","first-page":"7423","article-title":"An analysis on ensemble methods in classification tasks","volume":"3","author":"Gopika D.","year":"2014","journal-title":"International Journal of Advanced Research in Computer and Communication Engineering"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCI.2015.2471235"},{"key":"e_1_2_9_8_2","doi-asserted-by":"publisher","DOI":"10.4103\/0256-4602.64604"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2013.04.006"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.3233\/IDA-2005-9602"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.5755\/j01.itc.46.1.13051"},{"key":"e_1_2_9_12_2","doi-asserted-by":"crossref","unstructured":"KilimciZ. H. AkyokusS. andOmurcaS. \u0130. The evaluation of heterogeneous classifier ensembles for Turkish texts 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA) 2017 Gdynia 307\u2013311 https:\/\/doi.org\/10.1109\/INISTA.2017.8001176 2-s2.0-85030266160.","DOI":"10.1109\/INISTA.2017.8001176"},{"key":"e_1_2_9_13_2","unstructured":"YoungT. HazarikaD. PoriaS. andCambriaE. Recent trends in deep learning based natural language processing 2018 http:\/\/arxiv.org\/abs\/1708.02709v5."},{"key":"e_1_2_9_14_2","volume-title":"Machine Learning: ECML-98. ECML 1998. Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","author":"Joachims T.","year":"1998"},{"key":"e_1_2_9_15_2","doi-asserted-by":"crossref","unstructured":"LarkeyL. S.andBruce CroftW. Combining classifiers in text categorization Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR \u203296 1996 Zurich Switzerland 289\u2013297.","DOI":"10.1145\/243199.243276"},{"key":"e_1_2_9_16_2","doi-asserted-by":"crossref","unstructured":"DongY.-S.andHanK.-S. A comparison of several ensemble methods for text categorization IEEE International Conference onServices Computing 2004. (SCC 2004). Proceedings. 2004 2004 Shanghai China 419\u2013422 https:\/\/doi.org\/10.1109\/scc.2004.1358033.","DOI":"10.1109\/SCC.2004.1358033"},{"key":"e_1_2_9_17_2","doi-asserted-by":"crossref","unstructured":"FungG. P. C. Jeffrey XuY. WangH. CheungD. W. andLiuH. A balanced ensemble approach to weighting classifiers for text classification Sixth International Conference on Data Mining (ICDM\u203206) 2006 Hong Kong 869\u2013873 https:\/\/doi.org\/10.1109\/icdm.2006.2 2-s2.0-84878065641.","DOI":"10.1109\/ICDM.2006.2"},{"key":"e_1_2_9_18_2","first-page":"277","volume-title":"Machine Learning: ECML 2003. ECML 2003. Lecture Notes in Computer Science","author":"Liu Y.","year":"2003"},{"key":"e_1_2_9_19_2","doi-asserted-by":"crossref","unstructured":"KeretnaS. LimC. P. CreightonD. andShabanK. B. Classification ensemble to improve medical named entity recognition 2014 IEEE International Conference on Systems Man and Cybernetics (SMC) October 2014 San Diego CA USA 2630\u20132636 https:\/\/doi.org\/10.1109\/smc.2014.6974324 2-s2.0-84938060273.","DOI":"10.1109\/SMC.2014.6974324"},{"key":"e_1_2_9_20_2","doi-asserted-by":"crossref","unstructured":"GangehM. J. KamelM. S. andDuinR. P. W. Random subspace method in text categorization 2010 20th International Conference on Pattern Recognition August 2010 Istanbul Turkey 2049\u20132052 https:\/\/doi.org\/10.1109\/icpr.2010.505 2-s2.0-78149482605.","DOI":"10.1109\/ICPR.2010.505"},{"key":"e_1_2_9_21_2","doi-asserted-by":"publisher","DOI":"10.7763\/IJCCE.2012.V1.18"},{"key":"e_1_2_9_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.03.041"},{"key":"e_1_2_9_23_2","doi-asserted-by":"crossref","unstructured":"KanakarajM.andGuddetiR. M. R. Performance analysis of ensemble methods on Twitter sentiment analysis using NLP techniques Proceedings of the 2015 IEEE 9th International Conference on Semantic Computing (IEEE ICSC 2015) February 2015 Anaheim CA 169\u2013170 https:\/\/doi.org\/10.1109\/icosc.2015.7050801 2-s2.0-84925666945.","DOI":"10.1109\/ICOSC.2015.7050801"},{"key":"e_1_2_9_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2016.03.045"},{"key":"e_1_2_9_25_2","unstructured":"RezaeiniaS. M. GhodsiA. andRahmaniR. Improving the accuracy of pre-trained word embeddings for sentiment analysis 2017 http:\/\/arxiv.org\/abs\/1711.08609v1."},{"key":"e_1_2_9_26_2","unstructured":"MikolovT. ChenK. CorradoG. andDeanJ. Efficient estimation of word representations in vector space 2013 http:\/\/arxiv.org\/abs\/1301.3781."},{"key":"e_1_2_9_27_2","first-page":"3111","volume-title":"Advances in Neural Information Processing Systems","author":"Mikolov T.","year":"2013"},{"key":"e_1_2_9_28_2","doi-asserted-by":"crossref","unstructured":"PenningtonJ. SocherR. andManningC. GloVe: global vectors for word representation Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2014 Doha Qatar 1532\u20131543 https:\/\/doi.org\/10.3115\/v1\/d14-1162.","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_2_9_29_2","unstructured":"JoulinA. GraveE. BojanowskiP. andMikolovT. Bag of tricks for efficient text classification 2016 http:\/\/arxiv.org\/abs\/1612.03651."},{"key":"e_1_2_9_30_2","doi-asserted-by":"crossref","unstructured":"KalchbrennerN. GrefenstetteE. andBlunsomP. A convolutional neural network for modelling sentences Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2014 Baltimore MD USA 655\u2013666 https:\/\/doi.org\/10.3115\/v1\/p14-1062.","DOI":"10.3115\/v1\/P14-1062"},{"key":"e_1_2_9_31_2","doi-asserted-by":"crossref","unstructured":"KimY. Convolutional neural networks for sentence classification Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2014 Doha Qatar https:\/\/doi.org\/10.3115\/v1\/d14-1181.","DOI":"10.3115\/v1\/D14-1181"},{"key":"e_1_2_9_32_2","unstructured":"SantosC. N. D.andGattiM. Deep convolutional neural networks for sentiment analysis of short texts The 25th International Conference on Computational Linguistics August 2014 Dublin Ireland 69\u201378."},{"key":"e_1_2_9_33_2","unstructured":"ZhangX.andLeCunY. Text understanding from scratch 2015 http:\/\/arxiv.org\/abs\/1502.01710."},{"key":"e_1_2_9_34_2","unstructured":"ZhangX. ZhaoJ. andLeCunY. Character-level convolutional networks for text classification Advances in Neural Information Processing Systems 2015 Montreal Canada 649\u2013657."},{"key":"e_1_2_9_35_2","unstructured":"JohnsonR.andZhangT. Semi-supervised convolutional neural networks for text categorization via region embedding Advances in Neural Information Processing Systems 2015 Montreal Canada 919\u2013927."},{"key":"e_1_2_9_36_2","unstructured":"ConneauA. SchwenkH. BarraultL. andLecunY. Very deep convolutional networks for text classification 2017 http:\/\/arxiv.org\/abs\/1606.01781v2."},{"key":"e_1_2_9_37_2","unstructured":"KowsariK. BrownD. E. HeidarysafaM. MeimandiK. J. GerberM. S. andBarnesL. E. HDLTex: hierarchical deep learning for text classification http:\/\/arxiv.org\/abs\/1709.08267v2."},{"key":"e_1_2_9_38_2","first-page":"41","volume-title":"AAAI-98 Workshop on Learning for Text Categorization","author":"McCallum A.","year":"1998"},{"key":"e_1_2_9_39_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009715923555"},{"key":"e_1_2_9_40_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_9_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_2_9_42_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_2_9_43_2","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_44_2","doi-asserted-by":"crossref","unstructured":"JohnsonR.andZhangT. Effective use of word order for text categorization with convolutional neural networks Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2015 Denver CO USA https:\/\/doi.org\/10.3115\/v1\/n15-1011.","DOI":"10.3115\/v1\/N15-1011"},{"key":"e_1_2_9_45_2","doi-asserted-by":"publisher","DOI":"10.1023\/B:MACH.0000015881.36452.6e"},{"key":"e_1_2_9_46_2","unstructured":"Amasyal\u0131M. F.andBekenA. T\u00fcrk\u00e7e kelimelerin anlamsal benzerliklerinin \u00f6l\u00e7\u00fclmesi ve metin s\u0131n\u0131fland\u0131rmada kullan\u0131lmas\u0131 IEEE signal processing and communications applications conference 2009 Antalya Turkey."},{"key":"e_1_2_9_47_2","doi-asserted-by":"publisher","DOI":"10.1080\/18756891.2010.9727729"},{"key":"e_1_2_9_48_2","unstructured":"https:\/\/kdd.ics.uci.edu\/databases\/20newsgroups\/20newsgroups.html."},{"key":"e_1_2_9_49_2","unstructured":"CravenM. DiPasquoD. FreitagD. McCallumA. MitchellT. NigamK. andSlatteryS. A. Learning to extract symbolic knowledge from the world wide web Proceedings of the Fifteenth National\/Tenth Conference on Artificial Intelligence\/Innovative Applications of Artificial Intelligence. Menlo Park: American Association for Artificial Intelligence 1998 Menlo Park CA USA 509\u2013516."},{"key":"e_1_2_9_50_2","doi-asserted-by":"crossref","unstructured":"KilimciZ. H. AkyokusS. andOmurcaS. I. The effectiveness of homogenous ensemble classifiers for Turkish and English texts 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA) August 2016 Sinaia Romania 1\u20137 https:\/\/doi.org\/10.1109\/inista.2016.7571854 2-s2.0-84992126793.","DOI":"10.1109\/INISTA.2016.7571854"},{"key":"e_1_2_9_51_2","unstructured":"MikolovT. ChenK. CorradoG. andDeanJ. English pre-trained Word2vec model https:\/\/code.google.com\/archive\/p\/word2vec\/."},{"key":"e_1_2_9_52_2","unstructured":"KoksalA. Turkish pre-trained Word2vec model https:\/\/github.com\/akoksal\/Turkish-Word2Vec."},{"key":"e_1_2_9_53_2","doi-asserted-by":"crossref","unstructured":"PappagariR. VillalbaJ. andDehakN. Joint verificationidentification in end-to-end multi-scale cnn framework for topic identification 2018 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) April 2018 Calgary Alberta Canada https:\/\/doi.org\/10.1109\/icassp.2018.8461673.","DOI":"10.1109\/ICASSP.2018.8461673"},{"key":"e_1_2_9_54_2","doi-asserted-by":"crossref","unstructured":"SkianisK. MalliarosF. andVazirgiannisM. Fusing document collection and label graph-based representations with word embeddings for text classification Proceedings of the Twelfth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-12) June 2018 New Orleans Louisiana United States.","DOI":"10.18653\/v1\/W18-1707"},{"key":"e_1_2_9_55_2","doi-asserted-by":"crossref","unstructured":"ZhengS. Hongyun BaoJ. X. HaoY. QiZ. andHaoH. A bidirectional hierarchical skip-gram model for text topic embedding 2016 International Joint Conference on Neural Networks (IJCNN) July 2016 Vancouver BC 855\u2013862.","DOI":"10.1109\/IJCNN.2016.7727289"}],"container-title":["Complexity"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/7130146.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/complexity\/2018\/7130146.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2018\/7130146","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T19:13:06Z","timestamp":1775243586000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2018\/7130146"}},"subtitle":[],"editor":[{"given":"Ireneusz","family":"Czarnowski","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2018,1]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,1]]}},"alternative-id":["10.1155\/2018\/7130146"],"URL":"https:\/\/doi.org\/10.1155\/2018\/7130146","archive":["Portico"],"relation":{},"ISSN":["1076-2787","1099-0526"],"issn-type":[{"value":"1076-2787","type":"print"},{"value":"1099-0526","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1]]},"assertion":[{"value":"2018-04-17","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-09-25","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2018-10-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7130146"}}