论文标题
k-nearest邻居和支持向量机混合分类
K-Nearest Neighbour and Support Vector Machine Hybrid Classification
论文作者
论文摘要
在本文中，已经提出了一种新颖的K-Nearen邻居和支持向量机混合分类技术，该技术简单而坚固。它基于歧视性最近的邻里分类的概念。该技术包括使用k-nearest邻居分类来满足接近条件的测试样品。不通过接近条件的模式分开。接下来是根据欧几里得距离度量标准，将分别与每个分离的测试模式最接近的每个类别的固定数量模式筛选训练集。随后，对于每个分离的测试样本，都对与IT相关的筛选训练集模式进行了支持向量机，并完成了测试样本的分类。提出的技术已与该研究领域的最先进的技术进行了比较。三个数据集。美国邮政服务（USPS）手写数字数据集，MNIST数据集和阿拉伯数字数据集，修改后的阿拉伯数字数据库MADB已被用来评估算法的性能。该算法通常优于与之比较的其他算法。
In this paper, a novel K-Nearest Neighbour and Support Vector Machine hybrid classification technique has been proposed that is simple and robust. It is based on the concept of discriminative nearest neighbourhood classification. The technique consists of using K-Nearest Neighbour Classification for test samples satisfying a proximity condition. The patterns which do not pass the proximity condition are separated. This is followed by sifting the training set for a fixed number of patterns for every class which are closest to each separated test pattern respectively, based on the Euclidean distance metric. Subsequently, for every separated test sample, a Support Vector Machine is trained on the sifted training set patterns associated with it, and classification for the test sample is done. The proposed technique has been compared to the state of art in this research area. Three datasets viz. the United States Postal Service (USPS) Handwritten Digit Dataset, MNIST Dataset, and an Arabic numeral dataset, the Modified Arabic Digits Database, MADB, have been used to evaluate the performance of the algorithm. The algorithm generally outperforms the other algorithms with which it has been compared.
