Document Classification Algorithms: And Feature Selection Techniques with the Practical Test - Esraa Hussein,Ahmed Hussein
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Documents classification is one of the most important fields in Natural language processing and text mining. There are many algorithms can be used to perform this task. Most of the used algorithms are from machine learning like: Decision Tree, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes. These are the most essential four classification algorithms. Many researches try to modify and improve th ... Full description
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Description
Documents classification is one of the most important fields in Natural language processing and text mining. There are many algorithms can be used to perform this task. Most of the used algorithms are from machine learning like: Decision Tree, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes. These are the most essential four classification algorithms. Many researches try to modify and improve these algorithms for text classification. In this book, our work is divided into two levels: (i) a comparative study for these four algorithms, (ii) studying the improvement of document classification with feature selection where four feature selection methods are used and a new feature selection method is suggested.
More Information
| Author | Esraa Hussein, Ahmed Hussein |
|---|---|
| Publisher | LAP LAMBERT Academic Publishing |
| Release year | 2020 |
| Cover type | Softcover |
| EAN | 9786200785268 |