模糊基因:提高信息检索的有效性

M. Martín-Bautista, M. Vila, D. Sánchez, H. Larsen
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引用次数: 12

摘要

利用遗传算法和模糊逻辑提高了信息检索的有效性。在GAs框架下,给出了一种新的信息检索模型分类方法。这种分类是基于所选择的适应度函数的目标。当优化的目标是文档分类时,我们处理面向文档的模型。另一方面,面向术语的模型试图找到那些更具歧视性且足以满足用户偏好的术语来构建配置文件。针对第一类模型,提出了一种新的基于模糊逻辑的加权方案。并与其他经典加权方案进行了比较,研究了基因的局部适应度与每条染色体整体适应度的最佳聚合算子。在面向术语的模型中对这种新方案进行更深入的研究是今后工作的主要目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fuzzy genes: improving the effectiveness of information retrieval
An improvement in the effectiveness of information retrieval by using genetic algorithms (GAs) and fuzzy logic is demonstrated. A new classification of information retrieval models within the framework of GAs is given. Such a classification is based on the target of the fitness function selected. When the aim of the optimization is document classification, we deal with document-oriented models. On the other hand, term-oriented models attempt to find those terms that are more discriminatory and adequate for user preferences to build a profile. A new weighting scheme based on fuzzy logic is presented for the first class of models. A comparison with other classical weighting schemes and a study of the best aggregation operators of the gene's local fitness to the overall fitness per chromosome are also presented. The deeper study of this new scheme in the term-oriented models is the main objective for future work.
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