Detecting Concept Drift in Data Stream Using Semi-Supervised Classification
نویسندگان
چکیده
منابع مشابه
Handling Gradual Concept Drift in Stream Data
Data streams are sequence of data examples that continuously arrive at time-varying and possibly unbound streams. These data streams are potentially huge in size and thus it is impossible to process many data mining techniques (e.g., sensor readings, call records, web page visits). Tachiniques for classification fail to successfully process data streams because of two factors: their overwhelmin...
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Detecting concept drift in data streams has been widely studied in the data mining community. Conventional drift detection methods use classifiers’ outputs (e.g., classification accuracy, error rate) as indicators to signal concept changes. As a result, their performance greatly depends on the chosen classifiers. However, there is little work on addressing concept drift in graph-structured data...
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ژورنال
عنوان ژورنال: ?????? ????? ? ??????
سال: 2022
ISSN: ['2538-4201', '2538-421X']
DOI: https://doi.org/10.52547/jsdp.18.4.153