Performance of Classification Models on Various Types of Character Data
| Autor: | Eman Abdelfattah, Vanesa Getseva, Salil Maharjan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Decision tree Pattern recognition 01 natural sciences k-nearest neighbors algorithm Random forest 010309 optics 010104 statistics & probability Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Character (mathematics) 0103 physical sciences Pattern recognition (psychology) Artificial intelligence 0101 mathematics business |
| Zdroj: | UEMCON |
| DOI: | 10.1109/uemcon47517.2019.8993040 |
| Popis: | During the last six decades, numerous studies on the topic of pattern recognition in machines have been conducted. An important problem in pattern recognition with various real-life applications is character recognition. This study aims to compare the performance of five classification models on different character datasets. The models presented are K-Nearest Neighbors, Naive Bayes, Decision Trees, Random Forests, and Extra Trees. We concluded that Random Forests and Extra Trees have comparable results and they outperform the other models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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