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Concept Drift Adaptation by Exploiting Drift Type
ACM Transactions on Knowledge Discovery from DataConcept drift is a phenomenon where the distribution of data streams changes over time. When this happens, model predictions become less accurate. Hence, models built in the past need to be re-learned for the current data. Two design questions need to be addressed in designing a strategy to re-learn models: which type of concept drift has occurred, and
Jinpeng Li +4 more
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Concept Signatures and Semantic Drift
2011Ontology evolution is the process of incrementally and consistently adapting an existing ontology to changes in the relevant domain. Semantic drift refers to how ontology concepts’ intentions gradually change as the domain evolves. Normally, a semantic drift captures small domain changes that are hard to detect with traditional ontology management ...
Jon Atle Gulla +4 more
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2005
Traditional approaches to data mining are based on an assumption that the process that generated or is generating a data stream is static. Although this assumption holds for many applications, it does not hold for many others. Consider systems that build models for identifying important e-mail.
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Traditional approaches to data mining are based on an assumption that the process that generated or is generating a data stream is static. Although this assumption holds for many applications, it does not hold for many others. Consider systems that build models for identifying important e-mail.
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Concept Drift in documents and Formal Concept Analysis
2010Formal Concept Analysis (FCA) enables us to extract conceptual information from data and to visualize its structure in a lattice form. Here, we apply FCA to the analysis of a series of documents published periodically, in order to capture drifts of formal concepts occurred in the series by shifting the corresponding formal context as a viewpoint by ...
Yutaka Miyazaki, Yuzuru Tanaka
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DynamicWEB: Adapting to Concept Drift and Object Drift in COBWEB
2008Examining concepts that change over time has been an active area of research within data mining. This paper presents a new method that functions in contexts where concept drift is present, while also allowing for modification of the instances themselves as they change over time.
Joel Scanlan +2 more
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Addressing Concept-Evolution in Concept-Drifting Data Streams
2010 IEEE International Conference on Data Mining, 2010The problem of data stream classification is challenging because of many practical aspects associated with efficient processing and temporal behavior of the stream. Two such well studied aspects are infinite length and concept-drift. Since a data stream may be considered a continuous process, which is theoretically infinite in length, it is impractical
Mohammad M. Masud 0001 +6 more
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Concept Drift Awareness in Twitter Streams
2014 13th International Conference on Machine Learning and Applications, 2014Learning in non-stationary environments is not an easy task and requires a distinctive approach. The learning model must not only have the ability to continuously learn, but also the ability to acquired new concepts and forget the old ones. Additionally, given the significant importance that social networks gained as information networks, there is an ...
Joana Cósta +3 more
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Contrasting Explanation of Concept Drift
ESANN 2022 proceedings, 2022Fabian Hinder +3 more
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Journal of Information and Computational Science, 2013
Mining data stream are facing many challenges now, one of them is concept drift problem. In many practical applications, concept drift usually affects the classification performance for data stream, or even make the classifier failed. However, most of the proposed methods are mainly focusing on solving concept drift from the data value point of view ...
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Mining data stream are facing many challenges now, one of them is concept drift problem. In many practical applications, concept drift usually affects the classification performance for data stream, or even make the classifier failed. However, most of the proposed methods are mainly focusing on solving concept drift from the data value point of view ...
openaire +1 more source