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Online Multi-target regression trees with stacked leaf models
Mastelini, Saulo Martiello ; Barbon, Sylvio ;
André
Carlos
Ponce
de
Leon
Ferreira
de
Carvalho
arXiv.org, 2020-03
Ithaca: Cornell University Library, arXiv.org
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Título:
Online Multi-target regression trees with stacked leaf models
Autor:
Mastelini, Saulo Martiello
;
Barbon, Sylvio
;
André
Carlos
Ponce
de
Leon
Ferreira
de
Carvalho
Assuntos:
Algorithms
;
Computer Science - Learning
;
Data transmission
;
Decision trees
;
Distance learning
;
Machine learning
;
Predictions
;
Regression analysis
;
Statistics - Machine Learning
;
Strategy
É parte de:
arXiv.org, 2020-03
Descrição:
One of the current challenges in machine learning is how to deal with data coming at increasing rates in data streams. New predictive learning strategies are needed to cope with the high throughput data and concept drift. One of the data stream mining tasks where new learning strategies are needed is multi-target regression, due to its applicability in a high number of real world problems. While reliable and effective learning strategies have been proposed for batch multi-target regression, few have been proposed for multi-target online learning in data streams. Besides, most of the existing solutions do not consider the occurrence of inter-target correlations when making predictions. In this work, we propose a novel online learning strategy for multi-target regression in data streams. The proposed strategy extends existing online decision tree learning algorithm to explore inter-target dependencies while making predictions. For such, the proposed strategy, called Stacked Single-target Hoeffding Tree (SST-HT), uses the inter-target dependencies as an additional information source to enhance predictive accuracy. Throughout an extensive experimental setup, we evaluate our proposal against state-of-the-art decision tree-based algorithms for online multi-target regression. According to the experimental results, SST-HT presents superior predictive accuracy, with a small increase in the processing time and memory requirements.
Editor:
Ithaca: Cornell University Library, arXiv.org
Idioma:
Inglês
Links
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