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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >The Nearest Subclass Classifier: A Compromise between the Nearest Mean and Nearest Neighbor Classifier
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The Nearest Subclass Classifier: A Compromise between the Nearest Mean and Nearest Neighbor Classifier

机译：最近的子类分类器：最近均值和最近邻居分类器之间的折衷

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摘要

We present the Nearest Subclass Classifier (NSC), which is a classification algorithm that unifies the flexibility of the nearest neighbor classifier with the robustness of the nearest mean classifier. The algorithm is based on the Maximum Variance Cluster algorithm and, as such, it belongs to the class of prototype-based classifiers. The variance constraint parameter of the cluster algorithm serves to regularize the classifier, that is, to prevent overfitting. With a low variance constraint value, the classifier turns into the nearest neighbor classifier and, with a high variance parameter, it becomes the nearest mean classifier with the respective properties. In other words, the number of prototypes ranges from the whole training set to only one per class. In the experiments, we compared the NSC with regard to its performance and data set compression ratio to several other prototype-based methods. On several data sets, the NSC performed similarly to the k-nearest neighbor classifier, which is a well-established classifier in many domains. Also concerning storage requirements and classification speed, the NSC has favorable properties, so it gives a good compromise between classification performance and efficiency.
机译：我们提出了最近子分类器（NSC），这是一种分类算法，将最近邻分类器的灵活性与最近均值分类器的鲁棒性相结合。该算法基于最大方差聚类算法，因此，它属于基于原型的分类器。聚类算法的方差约束参数用于规范化分类器，即，防止过度拟合。在方差约束值较低的情况下，分类器将成为最近邻分类器，在方差参数较高的情况下，分类器将成为具有各自属性的最近均值分类器。换句话说，原型的数量从整个训练集到每个课程只有一个。在实验中，我们将NSC的性能和数据集压缩率与其他几种基于原型的方法进行了比较。在几个数据集上，NSC的表现类似于k最近邻分类器，它在许多领域都是公认的分类器。另外，在存储要求和分类速度方面，NSC具有良好的性能，因此可以很好地兼顾分类性能和效率。

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