Combining multiple classifiers, known as ensemble methods, can give substantial improvement in prediction performance of learning algorithms especially in the presence of non-informative features in the data sets. We propose an ensemble of subset of kNN classifiers, ESkNN, for classification task in two steps. Firstly, we choose classifiers based upon their individual performance using the out-of-sample accuracy. The selected classifiers are then combined sequentially starting from the best model and assessed for collective performance on a validation data set. We use bench mark data sets with their original and some added non-informative features for the evaluation of our method. The results are compared with usual kNN, bagged kNN, random kNN, multiple feature subset method, random forest and support vector machines. Our experimental comparisons on benchmark classification problems and simulated data sets reveal that the proposed ensemble gives better classification performance than the usual kNN and its ensembles, and performs comparable to random forest and support vector machines.

中文翻译：

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kNN分类器子集的集合。

组合多个分类器（称为集成方法）可以大大提高学习算法的预测性能，尤其是在数据集中存在非信息性特征的情况下。我们分两步提出了k个神经网络分类器子集ES k NN的集合。首先，我们使用样本外准确性根据分类器的个人表现来选择分类器。然后，从最佳模型开始依次组合选定的分类器，并根据验证数据集评估其总体性能。我们使用基准数据集及其原始数据和一些添加的非信息功能来评估我们的方法。将结果与通常的k NN（袋装k）进行比较NN，随机k NN，多特征子集方法，随机森林和支持向量机。我们对基准分类问题和模拟数据集的实验比较表明，与常规的k NN及其集合相比，所提出的集合具有更好的分类性能，并且其性能可与随机森林和支持向量机相媲美。