Abstract
The aim of this work was to compare the behavior of mutual information and Chi-square as metrics in the evaluation of the relevance of the terms extracted from documents related to “software design” retrieved from PubMed database tested in two contexts: using a set of terms retrieved from the vectorization of the corpus of abstracts and using only the terms retrieved from the vocabulary defined by the IEEE standard ISO/IEC/IEEE 24765. A search was conducted concerning the subject “software” in the last 6 years and we used Medical Subject Headings (Mesh) term “software design” of the articles to label them. Then mutual information and Chi-square metrics were computed as metrics to sort and select features. Chi-square obtained the highest accuracy scores in documents classification by using a multinomial naive Bayes classifier. Although these results suggest that Chi-square is better than mutual information in feature relevance estimation in the context of this work, further research is necessary to obtain a consistent foundation of this conclusion.
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Párraga-Valle, J., García-Bermúdez, R., Rojas, F., Torres-Morán, C., Simón-Cuevas, A. (2020). Evaluating Mutual Information and Chi-Square Metrics in Text Features Selection Process: A Study Case Applied to the Text Classification in PubMed. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_57
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