Deep learning- and word embedding-based heterogeneous classifier ensembles for text classification

Basit Öğe Kaydı
dc.authorid0000-0003-0793-1601
dc.contributor.authorKilimci, Zeynep Hilal
dc.contributor.authorAkyokuş, Selim
dc.date.accessioned10.07.201910:49:13
dc.date.accessioned2019-07-10T19:51:32Z
dc.date.available10.07.201910:49:13
dc.date.available2019-07-10T19:51:32Z
dc.date.issued2018
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.descriptionWOS: 000447892300001
dc.description.abstractThe 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-based 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 naive Bayes, support vector machine, and random forest and a deep learning-based 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.
dc.identifier.citationKilimci, Z. H. ve Akyokuş, S. (2018). Deep learning- and word embedding-based heterogeneous classifier ensembles for text classification. Complexity. https://dx.doi.org/10.1155/2018/7130116
dc.identifier.doi10.1155/2018/7130116
dc.identifier.issn1076-2787
dc.identifier.issn1099-0526
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://dx.doi.org/10.1155/2018/7130116
dc.identifier.urihttps://hdl.handle.net/20.500.12511/2236
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWiley-Hindawi
dc.relation.ispartofComplexityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsAttribution 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectSemantics
dc.subjectNatural Language Processing Systems
dc.subjectWord Representations
dc.titleDeep learning- and word embedding-based heterogeneous classifier ensembles for text classification
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Akyokuş, S..pdf
Boyut:
1.66 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Tam Metin / Full Text
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu
Makale Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu