K Important Neighbors: A Novel Approach to Binary Classification in High Dimensional Data.
K Important Neighbors: A Novel Approach to Binary Classification in High Dimensional Data.
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DOI：
10.1155/2017/7560807
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发表时间：
2017
影响因子：
--
通讯作者：
Zare N
中科院分区：
文献类型：
--
作者：
Raeisi Shahraki H;Pourahmad S;Zare N
K 最近邻 (KNN) 被称为最简单的非参数分类器之一，但在高维设置中，KNN 的准确性会受到干扰特征的影响。在这项研究中，我们提出了 K 重要邻居（KIN）作为高维问题二元分类的一种新方法。为了避免维数灾难，我们在初始阶段实施了平滑剪切绝对偏差（SCAD）逻辑回归，并考虑了每个特征在构建相异性度量中的重要性，并将特征贡献作为欧几里得距离上的 SCAD 系数的函数。这种混合相异性度量的本质结合了特征和距离信息，同时具有 SCAD 惩罚回归和 KNN 的所有良好特性。与KNN相比，仿真研究表明KIN在精度和降维方面都具有良好的性能。由于在构建相异性度量时利用了 SCAD 惩罚回归的预言机特性，我们发现所提出的方法能够消除几乎所有非信息性特征。在非常稀疏的设置中，KIN 的性能也优于支持向量机 (SVM) 和随机森林 (RF)，成为最佳分类器。
K nearest neighbors (KNN) are known as one of the simplest nonparametric classifiers but in high dimensional setting accuracy of KNN are affected by nuisance features. In this study, we proposed the K important neighbors (KIN) as a novel approach for binary classification in high dimensional problems. To avoid the curse of dimensionality, we implemented smoothly clipped absolute deviation (SCAD) logistic regression at the initial stage and considered the importance of each feature in construction of dissimilarity measure with imposing features contribution as a function of SCAD coefficients on Euclidean distance. The nature of this hybrid dissimilarity measure, which combines information of both features and distances, enjoys all good properties of SCAD penalized regression and KNN simultaneously. In comparison to KNN, simulation studies showed that KIN has a good performance in terms of both accuracy and dimension reduction. The proposed approach was found to be capable of eliminating nearly all of the noninformative features because of utilizing oracle property of SCAD penalized regression in the construction of dissimilarity measure. In very sparse settings, KIN also outperforms support vector machine (SVM) and random forest (RF) as the best classifiers.
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发表时间：
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影响因子：
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