Fuzzy classification based on combinative algorithms with fuzzy similarity measure / Nur Amira Mat Saffie
The performance of a single-model (classifier) can be determined on the basis of the classification accuracy. However, it is difficult to determine which single-model is the best classification technique in a specific application domain since a single learning algorithm may not uniformly outperform...
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| Format: | Thesis |
| Language: | en |
| Published: |
2019
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| Online Access: | https://ir.uitm.edu.my/id/eprint/87830/1/87830.pdf https://ir.uitm.edu.my/id/eprint/87830/ |
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| Summary: | The performance of a single-model (classifier) can be determined on the basis of the classification accuracy. However, it is difficult to determine which single-model is the best classification technique in a specific application domain since a single learning algorithm may not uniformly outperform other algorithms over various datasets. Furthermore, most classification algorithms, using either fuzzy or non-fuzzy approaches, produce results in the form of crisp or categorical classification outcomes. Moreover, in certain applications, the classification outcomes that represent class labels may involve categorisations which are fuzzy in nature. |
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