基于改进ML-KNN算法的文本分类研究

由于传统ML-KNN算法数据集中每个特征具有相同权重,与事实上的不同特征具有不同权重相违背,故提出对ML-KNN算法的改进,用ML-KNN来构建分类模型进行分类。为验证该算法的分类效果,选取算法常用的衡量标准与其他两种算法比较,结果表明由改进ML-KNN算法构造的模型要优于其他两种算法,能有效表达多领域数据集分类问题,算法效果更好。

Because each feature in the data set of the traditional ML-KNN algorithm has the same weight, which is contrary to the fact that different features have different weights, an improvement to the ML-KNN algorithm is proposed, which uses ML-KNN to build a classification model for classification. In order to verify the classification effect of the algorithm, the commonly used criteria of the algorithm are compared with the other two algorithms. The results show that the model constructed by the improved ML-KNN algorithm is better than the other two algorithms, and can effectively express the classification problem of multi-domain data sets, and the effect of the algorithm is better.