Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification
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 sy...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/7130146 |
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| author | Zeynep H. Kilimci Selim Akyokus |
| author_facet | Zeynep H. Kilimci Selim Akyokus |
| author_sort | Zeynep H. Kilimci |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9a0ec3102eff4a1889bbd82e65e4ffb4 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-9a0ec3102eff4a1889bbd82e65e4ffb42025-02-03T01:11:22ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/71301467130146Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text ClassificationZeynep H. Kilimci0Selim Akyokus1Computer Engineering Department, Dogus University, Istanbul 34722, TurkeyComputer Engineering Department, İstanbul Medipol University, Istanbul 34722, TurkeyThe 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.http://dx.doi.org/10.1155/2018/7130146 |
| spellingShingle | Zeynep H. Kilimci Selim Akyokus Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification Complexity |
| title | Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification |
| title_full | Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification |
| title_fullStr | Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification |
| title_full_unstemmed | Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification |
| title_short | Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification |
| title_sort | deep learning and word embedding based heterogeneous classifier ensembles for text classification |
| url | http://dx.doi.org/10.1155/2018/7130146 |
| work_keys_str_mv | AT zeynephkilimci deeplearningandwordembeddingbasedheterogeneousclassifierensemblesfortextclassification AT selimakyokus deeplearningandwordembeddingbasedheterogeneousclassifierensemblesfortextclassification |