‘Misclassification Error’ Greedy Heuristic to Construct Decision Trees for Inconsistent Decision Tables

Mohammad Azad, Mikhail Moshkov

2014

Abstract

A greedy algorithm has been presented in this paper to construct decision trees for three different approaches (many-valued decision, most common decision, and generalized decision) in order to handle the inconsistency of multiple decisions in a decision table. In this algorithm, a greedy heuristic ‘misclassification error’ is used which performs faster, and for some cost function, results are better than ‘number of boundary subtables’ heuristic in literature. Therefore, it can be used in the case of larger data sets and does not require huge amount of memory. Experimental results of depth, average depth and number of nodes of decision trees constructed by this algorithm are compared in the framework of each of the three approaches.

References

  1. Alcal-Fdez, J., Snchez, L., Garca, S., Jesus, M., Ventura, S., Garrell, J., Otero, J., Romero, C., Bacardit, J., Rivas, V., Fernndez, J., and Herrera, F. (2009). KEEL Multi Label Data Sets.
  2. 31 150 49 97 211 17 681 711 80 219 220 9 31 35 20 73 191 19 158 Azad, M., Chikalov, I., and Moshkov, M. (2013). Three approaches to deal with inconsistent decision tables - comparison of decision tree complexity. In RSFDGrC, pages 46-54.
  3. Bache, K. and Lichman, M. (2013). UCI machine learning repository.
  4. Blockeel, H., Schietgat, L., Struyf, J., Dzeroski, S., and Clare, A. (2006). Decision trees for hierarchical multilabel classification: A case study in functional genomics. In F ürnkranz, J., Scheffer, T., and Spiliopoulou, M., editors, PKDD 2006, Berlin, Germany, Proceedings, volume 4213 of LNCS, pages 18- 29. Springer.
  5. Boutell, M. R., Luo, J., Shen, X., and Brown, C. M. (2004). Learning multi-label scene classification. Pattern Recognition, 37(9):1757-1771.
  6. Clare, A. and King, R. D. (2001). Knowledge discovery in multi-label phenotype data. In PKDD, pages 42-53.
  7. Comité, F. D., Gilleron, R., and Tommasi, M. (2003). Learning multi-label alternating decision trees from texts and data. In MLDM, pages 35-49.
  8. Loza Mencía, E. and F ürnkranz, J. (2008). Pairwise learning of multilabel classifications with perceptrons. In IJCNN, pages 2899-2906.
  9. Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, Inc., NY, USA, 1 edition.
  10. Moshkov, M. and Zielosko, B. (2011). Combinatorial Machine Learning - A Rough Set Approach, volume 360 of Studies in Computational Intelligence. Springer.
  11. Pawlak, Z. (1991). Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht.
  12. Skowron, A. and Rauszer, C. (1992). The discernibility matrices and functions in information systems. In Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pages 331-362. Kluwer Academic Publishers, Dordrecht.
  13. Tsoumakas, G. and Katakis, I. (2007). Multi-label classification: An overview. IJDWM, 3(3):1-13.
  14. Tsoumakas, G., Katakis, I., and Vlahavas, I. P. (2010). Mining multi-label data. In Data Mining and Knowledge Discovery Handbook, 2nd ed., pages 667-685. Springer.
  15. Wieczorkowska, A., Synak, P., Lewis, R. A., and Ras, Z. W. (2005). Extracting emotions from music data. In ISMIS, pages 456-465.
  16. Zhou, Z.-H., Jiang, K., and Li, M. (2005). Multi-instance learning based web mining. Appl. Intell., 22(2):135- 147.
  17. Zhou, Z.-H., Zhang, M.-L., Huang, S.-J., and Li, Y.-F. (2012). Multi-instance multi-label learning. Artif. Intell., 176(1):2291-2320.
Download


Paper Citation


in Harvard Style

Azad M. and Moshkov M. (2014). ‘Misclassification Error’ Greedy Heuristic to Construct Decision Trees for Inconsistent Decision Tables . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 184-191. DOI: 10.5220/0005059201840191


in Bibtex Style

@conference{kdir14,
author={Mohammad Azad and Mikhail Moshkov},
title={‘Misclassification Error’ Greedy Heuristic to Construct Decision Trees for Inconsistent Decision Tables},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={184-191},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005059201840191},
isbn={978-989-758-048-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - ‘Misclassification Error’ Greedy Heuristic to Construct Decision Trees for Inconsistent Decision Tables
SN - 978-989-758-048-2
AU - Azad M.
AU - Moshkov M.
PY - 2014
SP - 184
EP - 191
DO - 10.5220/0005059201840191