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Feature reduction for neural network based text categorization
Abstract
In a text categorization model using an artificial neural network as the text classifier scalability is poor if the neural network is trained using the raw feature space since textural data has a very high-dimension feature space. We proposed and compared four dimensionality reduction techniques to reduce the feature space into an input space of much lower dimension for the neural network classifier. To test the effectiveness of the proposed model, experiments were conducted using a subset of the Reuters-22173 test collection for text categorization. The results showed that the proposed model was able to achieve high categorization effectiveness as measured by precision and recall. Among the four dimensionality reduction techniques proposed, principal component analysis was found to be the most effective in reducing the dimensionality of the feature space.
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“Furthermore, it becomes difficult to create a classifier for a large number of features [48] .”
From An efficient instance selection algorithm to reconstruct training set for support vector machine (2017) · cites this paper as [48]
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Metadata
- Authors
- Lam, S. L. Y., & Lee, D. L.
- Affiliations
- Hong Kong University of Science and Technology (HK)
- ORCID
- Dik Lun Lee
- Venue
- Proceedings. 6th International Conference on Advanced Systems for Advanced Applications
- Publisher
- IEEE Comput. Soc
- Published
- 2003-01-20
- Pages
- 195-202
- OpenAlex
- W2101154521
- Cited by
- 148
- References
- 12
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 6 · 2022: 3 · 2021: 4 · 2020: 3 · 2019: 4 · 2018: 15 · 2017: 10 · 2016: 3 · 2015: 4 · 2014: 7 · 2013: 10 · 2012: 4
- FWCI
- 4.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Database Systems for Advanced Applications (publishedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- text classification, feature reduction, dimensionality reduction, artificial neural networks, principal component analysis, high-dimensional feature space, scalability
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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