Adaptive edited natural neighbor algorithm
Adaptive edited natural neighbor algorithm
复制标题
自适应编辑自然邻域算法
DOI：
10.1016/j.neucom.2016.12.040
复制
发表时间：
2017-03
期刊：
影响因子：
6
通讯作者：
Dongdong Cheng
中科院分区：
文献类型：
--
作者：
Lijun Yang;Qingsheng Zhu;Jinlong Huang;Dongdong Cheng
约简技术可以降低用于分类模式的过高的计算成本和存储要求，同时保持分类精度。编辑的最近邻规则是最流行的归约技术之一，它可以去除最近邻未正确分类的噪声模式。然而，邻域参数的选取是传统邻域构造算法（如最近邻、ε-邻域等）未能解决的问题。为了解决这个问题，我们提出了一种新的编辑算法称为自适应编辑自然邻居算法（ENaN）。ENaN的目标是消除噪声模式的基础上的自然邻居的概念，通过自然邻居搜索算法自适应地获得。其主要优点是，ENaN不需要任何参数，可以降低噪声模式的效果。自适应ENaN算法可以很容易地应用到其他降噪算法中作为噪声滤波器。实验结果表明，该方法在有效去除噪声模式的同时，保持了更合理的类边界，提高了两种压缩方法的压缩精度和压缩率。
Reduction techniques can reduce prohibitive computational costs and the storage requirements for classifying patterns while maintaining classification accuracy. The edited nearest neighbor rule is one of the most popular reduction technique, which removes noisy patterns that are not correctly classified by theirk-nearest neighbors. However, selection of neighborhood parameters is an unsolved problem for the traditional neighborhood construction algorithms such ask-nearest neighbor andε-neighborhood. To solve the problem, we present a novel editing algorithm called adaptive Edited Natural Neighbor algorithm (ENaN). ENaN aims to eliminate the noisy patterns based on the concept of natural neighbor which are obtained adaptively by the search algorithm of natural neighbor. The main advantages are that ENaN does not need any parameters and can degrade the effect of noisy patterns. The adaptive ENaN algorithm can be easily applied into other reduction algorithms as a noisy filter. Experiments show that the proposed approach effectively removes the noisy patterns while keeping more reasonable class boundaries and improves the performance of two condensation methods in terms of both accuracy and reduction rate greatly.
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DOI：
10.1109/tnnls.2012.2198832
发表时间：
2012-05
影响因子：
10.4
作者：
K. Nikolaidis;Eduardo Rodríguez-Martínez;J. Y. Goulermas;Qing-Hua Wu
通讯作者：
K. Nikolaidis;Eduardo Rodríguez-Martínez;J. Y. Goulermas;Qing-Hua Wu
影响因子：
7.5
作者：
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通讯作者：
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DOI：
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发表时间：
2002-12
期刊：
Pattern Recognit.
影响因子：
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作者：
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通讯作者：
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DOI：
10.1109/icmlc.2011.6016939
发表时间：
2011-07
期刊：
2011 International Conference on Machine Learning and Cybernetics
影响因子：
--
作者：
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通讯作者：
Xiao-Meng Kang;Xiao-Peng Liu;Jun-Hai Zhai;Meng-Yao Zhai
影响因子：
6
作者：
L. Nanni;A. Lumini
通讯作者：
L. Nanni;A. Lumini