6 Conclusion
In this paper, we presented a comparative study of two different approaches of text
classification using supervised machine learning classifiers. We started by identifying
different methods of machine learning for text classification. Based on the literature
review, we presented a survey on the machine learning techniques proposed for text
classification. Through extensive experiments, we evaluated 6 methods based on our
proposed dataset in the epidemiological domain. To the best of our knowledge, this is
the first comparative study on scientific papers classification in the epidemiological
domain. We proceeded with a careful selection of the different scientific papers, was
made, based on a list of predefined classes according to the taxonomy of the epidemiological studies including: descriptive, analytical experimental and meta-analysis.
Based on our experimental results, we emphasize that the learning done on the Abstract
part (Introduction, Methods, Results, and Conclusion) is much more efficient than
working with full paper because the divergence of the subject in question.
References
1. Aha, D.W.: Editorial. Artif. Intell. Rev. 11(1–5), 7–10 (1997). https://doi.org/10.1023/A:
1006538427943
2. Cohen, K., Hunter, L.: Getting started in text mining. PLoS Comput. Biol. 4, e20 (2008).
https://doi.org/10.1371/journal.pcbi.0040020
3. Cutler, A., Cutler, D.R., Stevens, J.R.: Random forests. In: Zhang, C., Ma, Y. (eds.) Ensemble
Machine Learning, pp. 157–175. Springer, Boston (2012). https://doi.org/10.1007/978-14419-9326-7_5
4. Duong, H.T., Truong Hoang, V.: A survey on the multiple classifier for new benchmark
dataset of Vietnamese news classification, pp. 23–28, January 2019. https://doi.org/10.1109/
KST.2019.8687509
5. Mourya, A.K., ShafqatUlAhsaan, Kaur, H.: Performance and evaluation of different kernels in
support vector machine for text mining. In: Mohanty, M.N., Das, S. (eds.) Advances in
Intelligent Computing and Communication. LNNS, vol. 109, pp. 264–271. Springer,
Singapore (2020). https://doi.org/10.1007/978-981-15-2774-6_33
6. Thanh Noi, P., Kappas, M.: Comparison of random forest, k-nearest neighbor, and support
vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors 18
(2), 18 (2017). https://doi.org/10.3390/s18010018
Machine Learning Classification Models with SPD/ED Dataset
355
In this paper, we presented a comparative study of two different approaches of text
classification using supervised machine learning classifiers. We started by identifying
different methods of machine learning for text classification. Based on the literature
review, we presented a survey on the machine learning techniques proposed for text
classification. Through extensive experiments, we evaluated 6 methods based on our
proposed dataset in the epidemiological domain. To the best of our knowledge, this is
the first comparative study on scientific papers classification in the epidemiological
domain. We proceeded with a careful selection of the different scientific papers, was
made, based on a list of predefined classes according to the taxonomy of the epidemiological studies including: descriptive, analytical experimental and meta-analysis.
Based on our experimental results, we emphasize that the learning done on the Abstract
part (Introduction, Methods, Results, and Conclusion) is much more efficient than
working with full paper because the divergence of the subject in question.
References
1. Aha, D.W.: Editorial. Artif. Intell. Rev. 11(1–5), 7–10 (1997). https://doi.org/10.1023/A:
1006538427943
2. Cohen, K., Hunter, L.: Getting started in text mining. PLoS Comput. Biol. 4, e20 (2008).
https://doi.org/10.1371/journal.pcbi.0040020
3. Cutler, A., Cutler, D.R., Stevens, J.R.: Random forests. In: Zhang, C., Ma, Y. (eds.) Ensemble
Machine Learning, pp. 157–175. Springer, Boston (2012). https://doi.org/10.1007/978-14419-9326-7_5
4. Duong, H.T., Truong Hoang, V.: A survey on the multiple classifier for new benchmark
dataset of Vietnamese news classification, pp. 23–28, January 2019. https://doi.org/10.1109/
KST.2019.8687509
5. Mourya, A.K., ShafqatUlAhsaan, Kaur, H.: Performance and evaluation of different kernels in
support vector machine for text mining. In: Mohanty, M.N., Das, S. (eds.) Advances in
Intelligent Computing and Communication. LNNS, vol. 109, pp. 264–271. Springer,
Singapore (2020). https://doi.org/10.1007/978-981-15-2774-6_33
6. Thanh Noi, P., Kappas, M.: Comparison of random forest, k-nearest neighbor, and support
vector machine classifiers for land cover classification using sentinel-2 imagery. Sensors 18
(2), 18 (2017). https://doi.org/10.3390/s18010018
Machine Learning Classification Models with SPD/ED Dataset
355