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Machine Learning & Knowledge Discovery Group |
Multi-target RegressionIntroductionMulti-target or multi-output regression is another instance of the more general learning task of multi-target prediction. Here the prediction targets are real-valued, as opposed to the closely related task of multi-label clasification where the target variables are binary. There are numerous applications for multi-target regression such as stock market prediction, power generation forecasting and ecological modeling. Our contributionDespite its importance, multi-target regression has received relatively small attention from the Machine Learning community. The main reason is that multi-target regression is usually casted into independent regression problems and tackled with single-target regression algorithms. Our main contribution is the development new multi-target regression methods, which are inspired by well-known multi-label classification methods and are able to successfully model target dependencies. A further contribution is the creation of several new multi-target regression data sets, which can be used as benchmarks in future evaluations. Data sets and SoftwareThe multi-target regression data sets that we collected and used in our evaluations can be found here. The new multi-target regression methods and the evaluation framework have been implemented as an extension of Mulan. The code can currently be found in Mulan's SVN repository, while we plan a new release which will include several improvements in few weeks. Please contact Eleftherios Spyromitros-Xioufis for questions, comments, suggestions or request for help with the source code and the data sets. Publications
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