ABSTRACT

This letter proposes to use two k-nearest neighbours (k-NN) based algorithms namely: randomized k-NN and weighted k-NN for feature selection with two hyperspectral datasets. Two embedded feature selection methods, extreme gradient boosting and random forest classifier and one wrapper-based approach using support vector machine classifier, were used for comparison in terms of classification accuracy with selected features. For all classifications, a support vector machine classifier was used. Comparison of results in terms of classification accuracy with the selected features suggests better performance by randomized k-NN algorithm in comparison to weighted k-NN and other feature selection approaches used in this study. Results with both dataset also indicate that selected features by randomized k-NN based feature selection approach achieve higher accuracy than that with full datasets.

摘要

这封信建议使用两个基于k近邻（k -NN）的算法，即：随机k -NN和加权k -NN用于两个高光谱数据集的特征选择。两种嵌入式特征选择方法，极端梯度增强和随机森林分类器，以及一种使用支持​​向量机分类器的基于包装的方法，用于在分类精度上与所选特征进行比较。对于所有分类，均使用支持向量机分类器。根据分类精度与所选特征进行结果比较表明，与加权k相比，随机k -NN算法具有更好的性能-NN和本研究中使用的其他特征选择方法。两个数据集的结果还表明，通过基于随机k -NN的特征选择方法选择的特征比使用完整数据集的特征具有更高的准确性。