著者

長山 格 宮原 彬 島袋 航一

出版者

一般社団法人 電気学会

雑誌

電気学会論文誌. D, 産業応用部門誌 (ISSN:09136339)

巻号頁・発行日

vol.139, no.2, pp.158-165, 2019

被引用文献数

2

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<p>This paper proposes a new lazy learning algorithm, named balanced-kNN, for high performance robust classification of noisy patterns. K-nearest neighbor (k-NN) is a simple and powerful method with a high accuracy for various real world applications using unbiased datasets. However, noisy datasets are often gathered in real world applications. This paper presents a new robust algorithm, balanced-kNN, and compares the prediction accuracy with some conventional methods by using UCI datasets. The experimental results show that the balanced-kNN algorithm can perform more efficient classification of noisy data than the normal-kNN and weighted-kNN algorithms.</p>