Two-Stage Cost-Sensitive Learning for Data Streams With Concept Drift and Class Imbalance
نویسندگان
چکیده
منابع مشابه
Learning from Data Streams with Concept Drift Learning from Data Streams with Concept Drift
SUMMARY Increasing access to large, nonstationary datasets and corresponding demands to analyze these data has led to the development of new online algorithms for performing machine learning on data streams. An important feature of many real-world data streams is " concept drii, " whereby the characteristics of the data can change arbitrarily over time. e presence of concept drii in a data stre...
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Increasing access to incredibly large, nonstationary datasets and corresponding demands to analyse these data has led to the development of new online algorithms for performing machine learning on data streams. An important feature of real-world data streams is " concept drift, " whereby the distributions underlying the data can change arbitrarily over time. The presence of concept drift in a d...
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One of the most important challenges for machine learning community is to develop efficient classifiers which are able to cope with data streams, especially with the presence of the so-called concept drift. This phenomenon is responsible for the change of classification task characteristics, and poses a challenge for the learning model to adapt itself to the current state of the environment. So...
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Concept drift is a phenomenon typically experienced when data distributions change continuously over a period of time. In this paper we propose a cost-sensitive boosting approach for learning under concept drift. The proposed methodology estimates relevance costs of ‘old’ data samples w.r.t. to ‘newer’ samples and integrates it into the boosting process. We experiment this methodology on usenet...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3031603