Hybrid of Active Learning and Dynamic Self-Training for Data Stream Classification

  • Sogol Haghani Department of Computer Engineering and Data mining Lab Alzahra University Tehran, Iran
  • Mohammad Reza Keyvanpour Department of Computer Engineering Alzahra University Tehran, Iran
  • Mahnoosh Kholghi Computer Engineering Department, Member of young research club Islamic Azad University, Qazvin Branch Qazvin, Iran
Keywords: Computer Science, Data Mining, Semi-supervised learning, Classification; Data Stream


Most of the data stream classification methods need plenty of labeled samples to achieve a reasonable result. However, in a real data stream environment, it is crucial and expensive to obtain labeled samples, unlike the unlabeled ones. Although Active learning is one way to tackle this challenge, it ignores the effect of unlabeled instances utilization that can help with strength supervised learning. This paper proposes a hybrid framework named “DSeSAL”, which combines active learning and dynamic self-training to achieve both strengths. Also, this framework introduces variance based self-training that uses minimal variance as a confidence measure. Since an early mistake by the base classifier in self-training can reinforce itself by generating incorrectly labeled data, especially in multi-class condition. A dynamic approach to avoid classifier accuracy deterioration, is considered. The other capability of the proposed framework is controlling the accuracy reduction by specifying a tolerance measure. To overcome data stream challenges, i.e., infinite length and evolving nature, we use the chunking method along with a classifier ensemble. A classifier is trained on each chunk and with previous classifiers form an ensemble of M such classifiers. Experimental results on synthetic and real-world data indicate the performance of the proposed framework in comparison with other approaches.


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Author Biographies

Sogol Haghani, Department of Computer Engineering and Data mining Lab Alzahra University Tehran, Iran

received her B.S. in computer science from Kharazmi University, Tehran, Iran. She is currently working toward her master degree in the Department of computer engineering and data mining laboratory at Alzahra University, Tehran, Iran. Her research interests include data mining such as social networks and artificial neural networks.

Mohammad Reza Keyvanpour, Department of Computer Engineering Alzahra University Tehran, Iran

is an Associate Professor at Alzahra University, Tehran, Iran. He received his B.S. in software engineering from Iran University of Science & Technology, Tehran, Iran. He received his M. S. and Ph.D. in software engineering from Tarbiat Modares University, Tehran, Iran. His research interests include information retrieval and data mining.

Mahnoosh Kholghi, Computer Engineering Department, Member of young research club Islamic Azad University, Qazvin Branch Qazvin, Iran

received her B.S. in Software Engineering from Islamic Azad University, Karaj Branch, Karaj, Iran. She also received her M.S. in Software Engineering at Islamic Azad University, Qazvin Branch, Qazvin, Iran. Her research interests include Data Stream Mining and Machine Learning.


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How to Cite
Haghani, S., Keyvanpour, M. R., & Kholghi, M. (2018, August 11). Hybrid of Active Learning and Dynamic Self-Training for Data Stream Classification. International Journal of Information & Communication Technology Research, 9(4), 37-49. Retrieved from http://journal.itrc.ac.ir/index.php/ijictr/article/view/349
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