多目标基因编程策略以主题为基础的搜索,重点关注多样性和全球召回
Cecilia Baggio1,2, Carlos M Lorenzetti1,2, Rocío L Cecchini1,2
1Instituto de Ciencias e Ingeniería de la Computación (UNS-CONICET), Bahia Blanca, Buenos Aires, Argentina.
PeerJ. Computer science
|December 11, 2023
概括
本研究引入了基于主题的搜索系统的新多目标遗传编程策略,增强了查询生成以提高精度和回忆. 这种新的方法在提供准确和全面的搜索结果方面超越了现有的方法.
科学领域:
- 信息检索 信息检索
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 基于主题的搜索系统旨在通过了解用户的兴趣来获取相关信息.
- 自动生成多样化,精确和高回忆度的查询是一个重大挑战.
- 现有的多目标进化算法 (MOEA) 是有希望的,但在结果多样性和全球召回方面存在困难.
研究的目的:
- 提出新的多目标遗传编程 (MOGP) 策略,以改善基于主题的查询生成.
- 为了解决MOEA的局限性,特别是多样性丧失和全球召回率低.
- 开发客观的功能,最大限度地提高精度和回忆,同时最大限度地减少结果的相似性.
主要方法:
- 开发了一个MOGP策略家族.
- 定义了利用结果集相似性和信息的三个新型目标函数.
- 进行了广泛的实验,以评估拟议的战略.
主要成果:
- 拟议的MOGP策略在几代人内显著提高了精度.
- 一些策略成功地维持或增强了全球召回.
- 对比分析表明,MOGP方法在精度和全球召回方面优于以前的MOEA.
- 与最先进的术语权重方法相比,MOGP策略显示出更高的精度,回忆和F1得分.
结论:
- 拟议的MOGP策略为基于主题的查询生成提供了一种优越的方法.
- 策略的有效性取决于根据具体应用程序仔细选择最大化/最小化目标.
- 这项工作通过为基于主题的搜索提供更有效的工具,推进了信息检索领域.
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