Meta-Ensemble Classification Modeling for Concept Drift
نویسندگان
چکیده
منابع مشابه
Ensemble Classification for Drifting Concept
Traditional data mining classifiers are used for mining the static data, in which incremental learning assumed data streams come under stationary distribution where data concepts remain unchanged. The concept of data can be changed at any time in real world application this refers to change in the class definitions over time. Classifier ensembles are rapidly gaining popularity in data mining Co...
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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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The knowledge hidden in evolving data may change with time, this issue is known as concept drift. It often causes a learning system to decrease its prediction accuracy. Most existing techniques apply ensemble methods to improve learning performance on concept drift. In this paper, we propose a novel meta learning approach for this issue and develop a method: Multi-Step Learning (MSL). In our me...
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Knowledge extraction from data streams has attracted attention in recent years due to its wide range of applications, including sensor networks, web clickstreams, and user interest analysis. Concept drift is one of the most important research topics in data stream mining. Many algorithms that can adapt to concept drift have been proposed. However, most of them specialize in only one type of con...
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ژورنال
عنوان ژورنال: International Journal of Multimedia and Ubiquitous Engineering
سال: 2015
ISSN: 1975-0080
DOI: 10.14257/ijmue.2015.10.3.22