Online local boosting: Improving performance in online decision trees
Autor: | Victor G. Turrisi da Costa, Sylvio Barbon, Saulo Martiello Mastelini, André Castro Carvalho |
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Přispěvatelé: | Turrisi Da Costa, V. G., Martiello Mastelini, S., Ponce De Leon Ferreira De Carvalho, A. C., Barbon Junior, S |
Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
Boosting (machine learning)
Computer science Data stream mining business.industry Online learning Decision tree 020206 networking & telecommunications 02 engineering and technology Online Machine Learning Stream Mining Classification Machine learning computer.software_genre Time cost Continuous data 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
Zdroj: | BRACIS |
Popis: | As more data are produced each day, and faster, data stream mining is growing in importance, making clear the need for algorithms able to fast process these data. Data stream mining algorithms are meant to be solutions to extract knowledge online, specially tailored from continuous data problem. Many of the current algorithms for data stream mining have high processing and memory costs. Often, the higher the predictive performance, the higher these costs. To increase predictive performance without largely increasing memory and time costs, this paper introduces a novel algorithm, named Online Local Boosting (OLBoost), which can be combined into online decision tree algorithms to improve their predictive performance without modifying the structure of the induced decision trees. For such, OLBoost applies a boosting to small separate regions of the instances space. Experimental results presented in this paper show that by using OLBoost the online learning decision tree algorithms can significantly improve their predictive performance. Additionally, it can make smaller trees perform as good or better than larger trees. |
Databáze: | OpenAIRE |
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