基于SVM与局部加权的KNN算法的研究与实现

Research and implementation of KNN algorithm based on SVM and locally weighted

文章主要研究基于SVM与局部加权的KNN算法。在研究过程中,首先分析了KNN算法的基本概念和实现原理。在此基础上,引入SVM模型与局部加权方法对KNN算法进行改进,并将改进后的算法应用于乳腺癌识别的实例研究中。最后,在搭建的理论框架上进行了仿真实验。实验结果表明,与传统KNN算法相比,基于SVM与局部加权的KNN算法通过引入权重机制和SVM模型,在分类性能方面表现出更高的精准度,有效弥补了传统KNN算法在分类性能上的不足,显著提升了目标分类的精度。

The article mainly studies the KNN algorithm based on SVM and local weighting.In the research process,the basic concepts and implementation principles of the KNN algorithm were first analyzed.On this basis,SVM model and local weighting method are introduced to improve KNN algorithm,and the improved algorithm is applied to the case study of breast cancer recognition.Finally,simulation experiments were conducted on the constructed theoretical framework.The experimental results show that compared with the traditional KNN algorithm,the KNN algorithm based on SVM and local weighting exhibits higher accuracy in classification performance by introducing a weight mechanism and SVM model,effectively compensating for the shortcomings of the traditional KNN algorithm in classification performance and significantly improving the accuracy of target classification.