Micro-MetaStream: Algorithm selection for time-changing data

• Autor(a) principal: Rossi, André Luis Debiaso [UNESP]
• Data de Publicação: 2021
• Outros Autores: Soares, Carlos, Souza, Bruno Feres de, Ponce de Leon Ferreira de Carvalho, André Carlos
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.ins.2021.02.075; http://hdl.handle.net/11449/207483
• topic: Algorithm selection; Meta-learning; Time-changing data

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.