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- W3007071044 abstract "The distribution of data streams may change over time, which is called concept drift. Data stream mining algorithms need to detect and adapt to such changes quickly. This paper proposes a new online ensemble algorithm, Diversity and Identification for Dealing with Drifts(DIDD), to tackle the concept drift problem. During the process of concept drift, the data of two concepts exist simultaneously. DIDD uses a snapshot model to find new conceptual data and learn them with lower diversity. Experiments show that DIDD can adapt to new concept more quickly than other online ensemble methods. DIDD has achieved good results on various data sets with different types of concept drift." @default.
- W3007071044 created "2020-03-06" @default.
- W3007071044 creator A5043074622 @default.
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- W3007071044 date "2019-12-01" @default.
- W3007071044 modified "2023-10-16" @default.
- W3007071044 title "DIDD: Identifying and Learning New Conceptual Data with Lower Diversity" @default.
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- W3007071044 doi "https://doi.org/10.1109/ssci44817.2019.9002968" @default.
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