Abstract
In this work, we address several tasks of structured prediction and propose a new method for handling such tasks. Structured prediction is becoming important as data mining is dealing with increasingly complex data (images, videos, sound, graphs, text,...). Our method, k-NN for structured prediction (kNN-SP), is an extension of the well known k-nearest neighbours method and can handle three different structured prediction problems: multi-target prediction, hierarchical multi-label classification, and prediction of short time-series. We evaluate the performance of kNN-SP on several datasets for each task and compare it to the performance of other structured prediction methods (predictive clustering trees and rules). We show that, despite it’s simplicity, the kNN-SP method performs satisfactory on all tested problems.
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Pugelj, M., Džeroski, S. (2011). Predicting Structured Outputs k-Nearest Neighbours Method. In: Elomaa, T., Hollmén, J., Mannila, H. (eds) Discovery Science. DS 2011. Lecture Notes in Computer Science(), vol 6926. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24477-3_22
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DOI: https://doi.org/10.1007/978-3-642-24477-3_22
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