Abstract
There is a lot of work in text categorization using only the title and abstract of the papers. However, in a full paper there is a much larger amount of information that could be used to improve the text classification performance. The potential benefits of using full texts come with an additional problem: the increased size of the data sets.
To overcome the increased the size of full text data sets we performed an assessment study on the use of feature selection methods for full text classification. We have compared two existing feature selection methods (Information Gain and Correlation) and a novel method called k-Best-Discriminative-Terms. The assessment was conducted using the Ohsumed corpora. We have made two sets of experiments: using title and abstract only; and full text.
The results achieved by the novel method show that the novel method does not perform well in small amounts of text like title and abstract but performs much better for the full text data sets and requires a much smaller number of attributes.
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Notes
- 1.
We have used single words in our study but the k-BDT can also be used with other groupings of words like n-grams (n > 1), NERs, etc.
- 2.
It has been used in binary text classification but can also be adapted to non binary classification problems.
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Acknowledgements
This work was supported by the Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. This work was also partially funded by the ERDF through the COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961, and by National Funds through the FCT as part of project UID/EEA/50014/2013.
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Adriano Gonçalves, C., Lorenzo Iglesias, E., Borrajo, L., Camacho, R., Seara Vieira, A., Talma Gonçalves, C. (2019). Comparative Study of Feature Selection Methods for Medical Full Text Classification. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_49
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