A combined algorithm of K-means and MTRL for multi-class classification  

作　　者：XUE Mengfan HAN Lei PENG Dongliang 

机构地区：[1]School of Automation, Hangzhou Dianzi University

出　　处：《Journal of Systems Engineering and Electronics》2019年第5期875-885,共11页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China(61703131; 61703129; 61701148; 61703128)

摘　　要：The basic idea of multi-class classification is a disassembly method,which is to decompose a multi-class classification task into several binary classification tasks.In order to improve the accuracy of multi-class classification in the case of insufficient samples,this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs.Rest to disassemble the multi-class classification task into binary classification tasks.K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs.Finally,the sampled dataset is applied to the MTRL,and multiple binary classifiers are trained together.With the help of MTRL,this method can utilize the inter-task association to train the model,and achieve the purpose of improving the classification accuracy of each binary classifier.The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset,Wine dataset,Multiple Features dataset,Wireless Indoor Localization dataset and Avila dataset.The basic idea of multi-class classification is a disassembly method, which is to decompose a multi-class classification task into several binary classification tasks. In order to improve the accuracy of multi-class classification in the case of insufficient samples, this paper proposes a multi-class classification method combining K-means and multi-task relationship learning(MTRL).The method first uses the split method of One vs. Rest to disassemble the multi-class classification task into binary classification tasks. K-means is used to down sample the dataset of each task,which can prevent over-fitting of the model while reducing training costs. Finally, the sampled dataset is applied to the MTRL, and multiple binary classifiers are trained together. With the help of MTRL, this method can utilize the inter-task association to train the model, and achieve the purpose of improving the classification accuracy of each binary classifier. The effectiveness of the proposed approach is demonstrated by experimental results on the Iris dataset, Wine dataset, Multiple Features dataset, Wireless Indoor Localization dataset and Avila dataset.

关 键 词：machine LEARNING MULTI-CLASS classification K-MEANS MULTI-TASK RELATIONSHIP LEARNING (MTRL) OVER-FITTING 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]