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- W2896654126 abstract "Text categorization can be applied in many areas, such as Business Intelligence, sentiment analysis, social network monitoring, and security. Considering textual analysis techniques’ wide applications, it is important to improve these techniques to obtain more precise and reliable results. Combined Dissimilarity Spaces (CoDiS) model implements a system for text categorization with multiple classifiers trained on different dissimilarity spaces, aiming to overcome Bag-of-Words’ limitations. This study evaluates CoDiS performance when applying some parametric variations, such as the classifier, the data input format, and the dissimilarity measure. Macro F1 and Micro F1 are used to evaluate the performance, and the results show a significant difference among different configurations tested on CoDiS. Based on the statistical tests’ results, it is possible to conclude that the best scenario is to apply TFIDF to represent the datasets, Cosine as the dissimilarity measure, and SVM as the classifier. This scenario also outperformed literature multiple classifier systems: Bagging and Random Subspace." @default.
- W2896654126 created "2018-10-26" @default.
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- W2896654126 date "2018-07-01" @default.
- W2896654126 modified "2023-09-25" @default.
- W2896654126 title "An empirical analysis of Combined Dissimilarity Spaces" @default.
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- W2896654126 doi "https://doi.org/10.1109/ijcnn.2018.8489287" @default.
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