K-Nearest Neighbor classification for glass identification problem

M. Aldayel
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引用次数: 23

Abstract

The discovery of knowledge form criminal evidence databases is important in order to make effective criminological investigation. The aim of data mining is to extract knowledge from database and produce unambiguous and reasonable patterns. K-Nearest Neighbor (KNN) is one of the most successful data mining methods used in classification problems. Many researchers show that combining different classifiers through voting resulted in better performance than using single classifiers. This paper applies KNN to help criminological investigators in identifying the glass type. It also checks if integrating KNN with another classifier using voting can enhance its accuracy in indentifying the glass type. The results show that applying voting can enhance the KNN accuracy in the glass identification problem.
玻璃识别问题的k -最近邻分类
从犯罪证据数据库中发现知识对于有效地进行犯罪侦查具有重要意义。数据挖掘的目的是从数据库中提取知识,生成明确合理的模式。k -最近邻(KNN)是用于分类问题的最成功的数据挖掘方法之一。许多研究表明,通过投票组合不同的分类器比使用单个分类器效果更好。本文应用KNN来帮助犯罪调查人员识别玻璃类型。它还检查了是否将KNN与另一个使用投票的分类器集成可以提高其识别玻璃类型的准确性。结果表明,在玻璃识别问题中应用投票可以提高KNN的准确率。
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