Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification
Joint Authors
Kilimci, Zeynep Hilal
Akyokus, Selim
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-10-09
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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-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 naïve 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.
American Psychological Association (APA)
Kilimci, Zeynep Hilal& Akyokus, Selim. 2018. Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification. Complexity،Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1135616
Modern Language Association (MLA)
Kilimci, Zeynep Hilal& Akyokus, Selim. Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification. Complexity No. 2018 (2018), pp.1-10.
https://search.emarefa.net/detail/BIM-1135616
American Medical Association (AMA)
Kilimci, Zeynep Hilal& Akyokus, Selim. Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification. Complexity. 2018. Vol. 2018, no. 2018, pp.1-10.
https://search.emarefa.net/detail/BIM-1135616
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1135616