Micro-MetaStream: Algorithm selection for time-changing data
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Data
2021-07-01
Autores
Rossi, André Luis Debiaso [UNESP]
Soares, Carlos
Souza, Bruno Feres de
Ponce de Leon Ferreira de Carvalho, André Carlos
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Resumo
Data stream mining needs to deal with scenarios where data distribution can change over time. As a result, different learning algorithms can be more suitable in different time periods. This paper proposes micro-MetaStream, a meta-learning based method to recommend the most suitable learning algorithm for each new example arriving in a data stream. It is an evolution of MetaStream, which recommends learning algorithms for batches of examples. By using a unitary granularity, micro-MetaStream is able to respond more efficiently to changes in data distribution than its predecessor. The meta-data combines meta-features, characteristics describing recent data, with base-level features, the original variables of the new example. In experiments on real-world regression data streams, micro-metaStream outperformed MetaStream and a baseline method at the meta-level and frequently improved the predictive performance at the base-level.
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Algorithm selection, Meta-learning, Time-changing data
Como citar
Information Sciences, v. 565, p. 262-277.