Concept Drift Detection for Evolving Stream Data
نویسندگان
چکیده
منابع مشابه
Concept Drift Detection for Imbalanced Stream Data
Common statistical prediction models often require and assume stationarity in the data. However, in many practical applications, changes in the relationship of the response and predictor variables are regularly observed over time, resulting in the deterioration of the predictive performance of these models. This paper presents Linear Four Rates (LFR), a framework for detecting these concept dri...
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Data stream is a sequence of data generated from various information sources at a high speed and high volume. Classifying data streams faces the three challenges of unlimited length, online processing, and concept drift. In related research, to meet the challenge of unlimited stream length, commonly the stream is divided into fixed size windows or gradual forgetting is used. Concept drift refer...
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Data Streams are unbounded, sequential data instances that are generated very rapidly. The storage, querying and mining of such rapid flows of data is computationally very challenging. Data Stream Mining (DSM) is concerned with the mining of such data streams in real-time using techniques that require only one pass through the data. DSM techniques need to be adaptive to reflect changes of the p...
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Detecting changes of concept definitions in data streams and adapting classifiers to them is studied in this paper. Many previous research assume that examples in a data stream are always labeled. As it may be difficult to satisfy in practice, we introduce an approach that detects a concept drift in unlabeled data and retrain a classifier using a limited number of labeled examples. The usefulne...
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ژورنال
عنوان ژورنال: IEICE Transactions on Information and Systems
سال: 2011
ISSN: 0916-8532,1745-1361
DOI: 10.1587/transinf.e94.d.2288