迁徙鸟类基于优化的特征选择用于文本分类
Cem Kaya1,2, Zeynep Hilal Kilimci3, Mitat Uysal1
1Department of Software Engineering, Dogus University, Istanbul, Turkey.
PeerJ. Computer science
|December 16, 2024
概括
本研究介绍了MBO-NB,这是一种结合迁徙鸟类优化 (MBO) 和天真贝叶斯的新方法,用于高效的文本分类特征选择. 与现有技术相比,MBO-NB显著提高了准确性,并减少了计算负载.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 文本分类面临着高维特征集的挑战.
- 现有的特征选择方法往往缺乏效率和可扩展性.
- 低于最佳的准确性和高计算开销是常见的问题.
研究的目的:
- 提出一种新的方法,MBO-NB,用于有效的特征选择在文本分类.
- 为了提高计算效率和分类准确性.
- 为了解决当前技术在处理广泛的特征集方面的局限性.
主要方法:
- 迁徙鸟类优化 (MBO) 与天真贝叶斯分类器的整合.
- 使用信息获取算法进行预处理,以减少特征.
- 与现有技术进行比较分析,包括粒子集群优化 (PSO).
主要成果:
- MBO-NB显著减少了特征数量,从平均62,221个减少到2,089.
- 在特征减少和分类准确性方面表现出卓越的有效性.
- 在四个实验设置中,PSO的平均表现超过了6.9%.
结论:
- MBO-NB为文本分类功能选择提供了一个可扩展和有效的解决方案.
- 整合MBO和Naïve Bayes提供了一个全面的方法.
- 为更强大的系统推进文本分类技术做出了贡献.
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