A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization
Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The A...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://repository.ugm.ac.id/278953/1/Wardoyo_PA.pdf |
| Summary: | Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous
characteristics and high-element dimensions. Many pattern
classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf
Optimizer (GWO) for FS are the main topics of our thorough
assessment of the literature on the Swarm Intelligence (SI)
algorithm. Furthermore, it illustrates how the hybrid SI
technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms. |
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