Handling Concept Drift in Data Stream Classification
Ritika Jani1, Nirav Bhatt2, Chandni Shah3
1Ritika Jani, Master degree in Information Technology from Chandubhai S Patel Institute of Technology, CHARUSAT University, India.
2Nirav Bhatt, Department of Information Technology in Chandubhai S Patel Institute of Technology, CHARUSAT, University, India.
3Chandni Shah, Department of Information Technology in Chandubhai S Patel Institute of Technology, CHARUSAT, University, India.
Manuscript received on 05 July 2019 | Revised Manuscript received on 09 July 2019 | Manuscript published on 30 August 2019 | PP: 548-550 | Volume-8 Issue-10, August 2019 | Retrieval Number: J88570881019/2019©BEIESP | DOI: 10.35940/ijitee.J8857.0881019
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Data Streams are having huge volume and it can-not be stored permanently in the memory for processing. In this paper we would be mainly focusing on issues in data stream, the major factors which are affecting the accuracy of classifier like imbalance class and Concept Drift. The drift in Data Stream mining refers to the change in data. Such as Class imbalance problem notifies that the samples are in the classes are not equal. In our research work we are trying to identify the change (Drift) in data, we are trying to detect Imbalance class and noise from changed data. And According to the type of drift we are applying the algorithms and trying to make the stream more balance and noise free to improve classifier’s accuracy.
Keywords: Data Stream mining, classification, semi supervised learning, concept drift.
Scope of the Article: Classification