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Learning from Imbalanced Data Streams
2018Mining data streams is one of the most vital fields in the contemporary ML. Increasing number of real-world problems are characterized by both volume and velocity of data, as well as by evolving characteristics. Learning from data stream assumes that new instances arrive continuously and that their properties may change over time due to a phenomenon ...
Alberto Fernández +5 more
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Boosting classifications with imbalanced data
2017Boosting is an ensemble method which uses a weak classifier to create a strong one, based on the theory of Robert Schapire s work in 1990 (see Schapire 1990). It appears similar to bagging yet is fundamentally different. This thesis will start with a short introduction followed by a chapter describing the theory and methodology behind ...
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Feature Selection in Imbalanced Data
Annals of Data Science, 2022Firuz Kamalov +2 more
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Imbalanced Classification for Big Data
2018New developments in computation have allowed an explosion for both data generation and storage. The high value that is hidden within this large volume of data has attracted more and more researchers to address the topic of Big Data analytics. The main difference between addressing Big Data applications and carrying out traditional DM tasks is ...
Alberto Fernández +5 more
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Imbalanced Data for Knowledge Tracing
2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2023Jyun-Yi Chen, I-Wei Lai
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An overview of real‐world data sources for oncology and considerations for research
Ca-A Cancer Journal for Clinicians, 2022Lynne Penberthy +2 more
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