Similarity-Based Synthetic Document Representations for Meta-Feature Generation in Text Classification
| Autor: | Thiago Salles, Thierson Couto Rosa, Marcos André Gonçalves, Sergio Canuto |
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| Rok vydání: | 2019 |
| Předmět: |
Hyperplane
Similarity (network science) business.industry Computer science 020204 information systems Feature vector 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Pattern recognition 02 engineering and technology Artificial intelligence Feature generation business |
| Zdroj: | SIGIR |
| DOI: | 10.1145/3331184.3331239 |
| Popis: | We propose new solutions that enhance and extend the already very successful application of meta-features to text classification. Our newly proposed meta-features are capable of: (1) improving the correlation of small pieces of evidence shared by neighbors with labeled categories by means of synthetic document representations and (local and global) hyperplane distances; and (2) estimating the level of error introduced by these newly proposed and the existing meta-features in the literature, specially for hard-to-classify regions of the feature space. Our experiments with large and representative number of datasets show that our new solutions produce the best results in all tested scenarios, achieving gains of up to 12% over the strongest meta-feature proposal of the literature. |
| Databáze: | OpenAIRE |
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