Classification by Instance-Based Learning Algorithm
Classification by Instance-Based Learning Algorithm
复制标题
基于实例的学习算法分类
DOI：
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发表时间：
2005
期刊：
影响因子：
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通讯作者：
Xiaoyong Du
中科院分区：
文献类型：
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作者：
Y. Bao;Eisuke Tsuchiya;N. Ishii;Xiaoyong Du
基本的 k 最近邻分类算法在许多领域都表现良好，但也有一些缺点。本文提出了一种基于实例的宽容学习算法TIBL及其简单投票的TIBL组合方法，该算法是遗传算法、宽容粗糙集和k近邻分类算法的集成。与基本的 k 最近邻算法和其他学习模型相比，所提出的算法旨在减少存储需求并提高泛化精度。已经在 UCI 机器学习存储库的一些基准数据集上进行了实验。结果表明，TIBL算法及其组合方法提高了k近邻分类的性能，并且比其他流行的机器学习算法获得了更高的泛化精度。
The basic k-nearest-neighbor classification algorithm works well in many domains but has several shortcomings. This paper proposes a tolerant instance-based learning algorithm TIBL and it’s combining method by simple voting of TIBL, which is an integration of genetic algorithm, tolerant rough sets and k-nearest neighbor classification algorithm. The proposed algorithms seek to reduce storage requirement and increase generalization accuracy when compared to the basic k-nearest neighbor algorithm and other learning models. Experiments have been conducted on some benchmark datasets from the UCI Machine Learning Repository. The results show that TIBL algorithm and it’s combining method, improve the performance of the k-nearest neighbor classification, and also achieves higher generalization accuracy than other popular machine learning algorithms.