Predicting Structured Outputs k-Nearest Neighbours Method

  • Mitja Pugelj
  • Sašo Džeroski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6926)

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.

Keywords

Near Neighbor Dynamic Time Warping Structure Output Descriptive Attribute Short Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mitja Pugelj
    • 1
  • Sašo Džeroski
    • 1
  1. 1.Department of Knowledge TechnologiesJožef Stefan InstituteLjubljanaSlovenia

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