201. Micro-MetaStream: Algorithm selection for time-changing data.
- Author
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Rossi, André Luis Debiaso, Soares, Carlos, Souza, Bruno Feres de, and Ponce de Leon Ferreira de Carvalho, André Carlos
- Subjects
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MACHINE learning , *DATA mining , *DATA distribution , *ALGORITHMS , *METADATA - Abstract
• Micro-MetaStream selects the best learning algorithm for each example of data streams. • This method relates data characteristics and the performance of regression models over time. • It combines characteristics from training data and raw features from the predictive attributes of each test example. • Predictive performance was improved when compared with two other meta-learning methods. 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. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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