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Normalized class coherence change-based kNN for classification of imbalanced data
Pattern Recognition ( IF 8 ) Pub Date : 2021-06-29 , DOI: 10.1016/j.patcog.2021.108126
Kyoungok Kim

kNN is a widely used machine learning algorithm in many different domains because of its fairly good performance in actual cases and its simplicity. This study aims to enhance the performance of kNN for imbalanced datasets, a topic that has been relatively ignored in kNN research. The proposed kNN algorithm, called normalized class coherence change-based k-nearest neighbor (NCC-NN) algorithm, determines the label of a test sample by computing the normalized class coherence changes at class and sample levels for every possible class and assigning the sample to the class with the maximum value. It considers the tendency that the minority classes usually show the lower-class coherence than the majority class. NCC-kNN also utilizes the adaptive k for the class coherence, which is calculated in a weighted manner to reduce the sensitivity to the selection of k. NCC-kNN was applied to 20 benchmark datasets with varying class imbalance and coherence, and its performance was compared with that of five kNN algorithms, SMOTE and MetaCost with standard kNN as a base classifier. The proposed NCC-kNN outperformed the other kNN algorithms in classification of imbalanced data, especially for imbalanced data with low positive class coherence.



中文翻译:

基于归一化类一致性变化的 k NN 用于不平衡数据分类

NN 是一种在许多不同领域中广泛使用的机器学习算法,因为它在实际情况下具有相当好的性能和简单性。本研究旨在提高用于不平衡数据集的 NN,这是一个相对被忽略的主题 神经网络研究。拟议的NN 算法,称为归一化类一致性基于变化 -最近邻(NCC-NN)算法,通过计算每个可能类在类和样本级别的归一化类一致性变化并将样本分配给具有最大值的类来确定测试样本的标签。它考虑了少数阶级通常比多数阶级表现出较低阶级一致性的趋势。NCC-NN 还利用自适应 对于类一致性,以加权方式计算以降低对选择的敏感性 . NCC-将 NN 应用于 20 个具有不同类别不平衡和一致性的基准数据集,并将其性能与五个 NN 算法、SMOTE 和 MetaCost 标准 NN 作为基分类器。提议的 NCC-NN 的表现优于其他 神经网络算法在不平衡数据分类中,特别是对于具有低正类一致性的不平衡数据。

更新日期:2021-07-24
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