The Analysis of Attribution Reduction of K-Nearest Neighbor (KNN) Algorithm by Using Chi-Square
The Analysis of Attribution Reduction of K-Nearest Neighbor (KNN) Algorithm by Using Chi-Square
复制标题
利用卡方分析K近邻(KNN)算法的归因约简
DOI：
10.1088/1742-6596/1424/1/012004
复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
Rahmat Widia Sembiring
中科院分区：
文献类型：
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作者：
Muhammad Danil;S. Efendi;Rahmat Widia Sembiring
数据约简是一种适用的技术，用于从体积小得多的数据中获得约简表示，但仍保持数据的原始完整性。属性约简是识别和消除具有不相关或过多值的属性的过程。在本研究中，属性约简是使用卡方算法实现的K-最近邻（KNN）进行分类的对象的基础上最接近的数据的对象。测试进行了皮马印第安人数据集的总768个数据。卡方检验方法用于减少大型数据集的维数，并提高最近K近邻结果的预测精度。K-最近邻使用卡方作为特征的选择被证明是准确和有效的减少数据。研究结果表明，采用卡方作为特征选择的K近邻算法在不降低原始数据完整性和信息质量的前提下，能够准确有效地减少数据量。
Data reduction is one of the applicable techniques used to obtain the reduction representation from the data whose volume is much smaller, but still retains the original integrity of the data. Attribute reduction is a process to identify and eliminate an attribute with irrelevant or excessive values. In this study, attribute reduction was carried out using the Chi-Square Algorithm implemented in K-Nearest Neighbor (KNN) for classifying the objects based on the closest data to the objects.The test was carried out on the Pima Indians dataset with the total of 768 data. The Chi-Square method was used to reduce the dimensions of large datasets and to improve the accuracy of the prediction of the closest K-Nearest Neighbor results. K-Nearest Neighbor using Chi-Square as the choice of features proved to be accurate and effective in reducing data. The results of this study show that K-Nearest Neighbor using Chi-Square as the choice of features was accurate and effective in reducing data without reducing the integrity of the original data and reducing the quality of the information produced.