Detecting Concept Drift in Data Stream Using Semi-Supervised Classification

‫و‬ ‫زاهدی‬ ‫مرتضی‬ 1 ‫ایران‬ ‫شاهرود،‬ ‫شاهرود،‬ ‫صنعتی‬ ‫دانشگاه‬ ‫کامپیوتر،‬ ‫مهندسی‬ ‫دانشکده‬ ‫سال‬ 1400 ‫شمارة‬ 4 ‫پیاپی‬ 50 154require receiving the labels from the user.In contrast to many of the current studies, the proposed algorithm uses an ensemble of K-NN classifiers.It constructs a group of clustering-based classification models, each of which is trained on a batch of data.On receiving each new sample, first it is determined whether the data sample is an outlier or not.If the data is included in a cluster, the sample class is determined by majority voting.When a window of the stream is received, the possibility of concept drift is examined based on entropy variation, and the classifier is updated by a semi-supervised approach if necessary.The model itself determines the required data labels.The proposed method is capable of detecting concept drift in data, and improving its accuracy via updating the learning model with appropriate samples received from the stream.Therefore, the proposed method only requires a small initial labeled data.Experiments are performed using five real and synthetic datasets, and the model performance is compared to three other approaches.The results show that the proposed method is superior in terms of precision, recall and F1 score compared to other studies.