Online Multi-target regression trees with stacked leaf models
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The amount of available data raises at large steps. Developing machine learning strategies to cope with the high throughput and changing data streams is a scope of high relevance. Among the prediction tasks in online machine learning, multi-target regression has gained increased attention due to its high applicability and relation with real-world problems. While reliable and effective solutions have been proposed for batch multi-target regression, the few existing solutions in the online scenario present gaps which should be further investigated. Among these problems, none of the existing solutions consider the occurrence of inter-target correlations when making predictions. In this work, we propose an extension to existing decision tree based solutions in online multi-target regression which tackles the problem mentioned above. Our proposal, called Stacked Single-target Hoeffding Tree (SST-HT) uses the inter-target dependencies as an additional information source to enhance accuracy. Throughout an extensive experimental setup, we evaluate our proposal against state-of-the-art decision tree-based solutions for online multi-target regression tasks on sixteen datasets. Our observations show that SST-HT is capable of achieving significantly smaller errors than the other methods, whereas only increasing the needed time and memory requirements in small amounts.
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