A Concept Drift Data Stream Classification Algorithm Based on Local Classification Accuracy
The classification of concept drift data streams is a challenging problem.When a new concept ap-pears,there are too few learning samples of the concept,and the classifier cannot be adjusted in time,which leads to low classification accuracy.In order to solve this problem,this article proposes a concept drift data stream classification algorithm,called LA-MS-CDC,based on local classification accuracy.Firstly,LA-MS-CDC combines k-means clustering and local classification accuracy algorithm to select the optimal source do-main classifier from the classifier pool.Secondly,the optimal source domain classifier and the target domain classifier are weighted and integrated to classify the samples.Then,according to the real labels of the classifi-cation samples,the loss of each classifier is calculated respectively and the weights of the classifiers in the tar-get domain and the source domain are updated.Then,the classification samples are used to update the target domain classifier and the optimal source domain classifier.Finally,the update of the classifier pool is comple-ted.The experimental results on the public datasets show that LA-MS-CDC can effectively transfer the source domain knowledge to the target domain,and the classification effect of LA-MS-CDC is significantly improved compared with the existing methods.The algorithm code can be obtained on https://gitee.com/ymw12345/LAMSCDC.
concept driftmulti-source online transfer learninglocal classification accuracyensemble learn-ingdiversity
万方数据
维普
