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, the existing solutions do not consider the occurrence of inter-target correlations when making predictions. In this work, we propose an online learning strategy for multi-target regression in data streams. The proposed strategy extends a single target decision tree online learning algorithm to allow multi-target online regression learning in data streams. 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.
@cite_11: Many real life prediction problems involve predicting a structured output. Multi-target regression is an instance of structured output prediction whose task is to predict for multiple target variables. Structured output algorithms are usually computationally and memory demanding, hence are not suited for dealing with massive amounts of data. Most of these algorithms can be categorized as local or global methods. Local methods produce individual models for each output component and combine them to produce the structured prediction. Global methods adapt traditional learning algorithms to predict the output structure as a whole. We propose the first rule-based algorithm for solving multi-target regression problems from data streams. The algorithm builds on the adaptive model rules framework. In contrast to the majority of the structured output predictors, this particular algorithm does not fall into the local and global categories. Instead, each rule specializes on related subsets of the output attributes. To evaluate the performance of the proposed algorithm, two other rule-based algorithms were developed, one using the local strategy and the other using the global strategy. These methods were compared considering their prediction error, memory usage, computational time, and model complexity. Experimental results on synthetic and real data show that the local-strategy algorithm usually obtains the lowest error. However, the proposed and the global-strategy algorithms use much less memory and run significantly much faster at the cost of a slightly increase in the error, which make them very attractive when computation resources are an important factor. Also, the models produced by the latter approaches are much easier to understand since considerably less rules are produced.
@cite_11 also expanded the AMRules framework for dealing with MTR tasks. The authors also employed the idea that the created partitions must reduce the variance in the output space. But different from the previous solution, AMRules does not lie in the global local categorization. Instead, the MTR version of AMRules is capable of specializing in subsets of targets by performing as follows: when executing a rule expansion test, if the variance in the target space is reduced only for some targets, a new decision rule encompassing the targets benefited by the split is created; a complementary rule without the expansion is also created for the remaining targets. Therefore, AMRules creates decision rules which can encompass all the targets, some of them, or even a single target, hence being a hybrid of a local and global method. AMRules was also adapted to deal with multi-label classification tasks .