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Learning Multi-Label Predictors under Sparsity Budget

Pekka Naula, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Learning Multi-Label Predictors under Sparsity Budget. In: Anders Kofod-petersen, Fredrik Heintz, Helge Langseth (Eds.), Proceedings of the Eleventh Scandinavian Conference on Artificial Intelligence (SCAI 2011), Frontiers in Artificial Intelligence and Applications 227, 30–39, IOS Press, 2011.

Abstract:

In real-life machine learning applications, there are often costs associated with the features needed in prediction. This is the case for example when deploying learned models in mass produced products, where the manufacturing costs or space limitations may restrict the number of feature extracting sensors that can be included in each device. In such situations, the training process involves a sparsity budget restricting the number of features the learned predictor can use. In this paper, we consider the problem of learning multi-label predictors under a sparsity budget. For this purpose, we consider three different wrapper-based greedy forward selection approaches for constructing sparse multi-label learning models. In our experiments, we show that the method selecting a common set of features shared by multiple tasks by greedily maximizing the prediction performance averaged over all the tasks provides a better prediction performance than the approaches selecting the features separately for each task.

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BibTeX entry:

@INPROCEEDINGS{iNaPaAiSa11a,
  title = {Learning Multi-Label Predictors under Sparsity Budget},
  booktitle = {Proceedings of the Eleventh Scandinavian Conference on Artificial Intelligence (SCAI 2011)},
  author = {Naula, Pekka and Pahikkala, Tapio and Airola, Antti and Salakoski, Tapio},
  volume = {227},
  series = {Frontiers in Artificial Intelligence and Applications},
  editor = {Kofod-petersen, Anders and Heintz, Fredrik and Langseth, Helge},
  publisher = {IOS Press},
  pages = {30–39},
  year = {2011},
  keywords = {Feature subset selection, multi-label learning, budgeted learning, regularized least-squares},
}

Belongs to TUCS Research Unit(s): Algorithmics and Computational Intelligence Group (ACI), Turku BioNLP Group

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