Combined feature selection and similarity modelling in case-based reasoning using hierarchical memetic algorithm

N. Xiong, P. Funk
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引用次数: 30

Abstract

This paper proposes a new approach to discover knowledge about key features together with their degrees of importance in the context of case-based reasoning. A hierarchical memetic algorithm is designed for this purpose to search for the best feature subsets and similarity models at the same time. The objective of the memetic search is to optimize the possibility distributions derived for individual cases in the case library under a leave-one-out procedure. The information about the importance of selected features is revealed from the magnitudes of parameters of the learned similarity model. The effectiveness of the proposed approach has been shown by evaluation results on the benchmark data sets from the UCI repository and in comparisons with other machine learning techniques.
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基于案例推理的层次模因算法特征选择与相似性建模相结合
本文提出了一种在基于案例的推理中发现关键特征及其重要程度的新方法。为此设计了一种分层模因算法,同时搜索最佳特征子集和相似度模型。模因搜索的目标是优化案例库中个别案例在“留一”过程下的可能性分布。所选特征的重要性信息是通过学习到的相似度模型参数的大小来揭示的。通过对来自UCI存储库的基准数据集的评估结果以及与其他机器学习技术的比较,表明了所提出方法的有效性。
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