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- W4313546722 abstract "In multi-label text classification, the central aim is to associate an array of descriptive labels for a better understanding of the text. There are three main challenges in doing multi-label text classification (i) a large number of text (input) features, (ii) the underlying implicit relationship between input features and output labels, and (iii) an implicit inter-label dependency. In traditional approaches to multi-label classification, these problems are not being addressed collectively. A feature selection strategy that inherently uses local features to discriminate a class and similarly global features that can distinctly separate classes can be very effective for multi-label classification. In this research, we perform a feature selection and ranking strategy based on local and global features. A Naïve Bayes classifier is being used using a combination of these two -feature sets, it is compared with the baseline implemented with the term frequency-inverse document frequency (TF-IDF). A series of experiments have been carried out on standard multi-label text datasets, using evaluation metrics like Hamming loss, Subset Accuracy and Micro/Macro F1 scores, and encouraging results are obtained." @default.
- W4313546722 created "2023-01-06" @default.
- W4313546722 creator A5008482541 @default.
- W4313546722 creator A5086471874 @default.
- W4313546722 date "2022-11-22" @default.
- W4313546722 modified "2023-10-16" @default.
- W4313546722 title "Learning Local and Global Features for Optimized Multi-Label Text Classification" @default.
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- W4313546722 doi "https://doi.org/10.1109/acit57182.2022.9994130" @default.
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