Evaluating Conditional and Unconditional Correlations Capturing Strategies in Multi Label Classification

Authors

  • Raed Alazaidah School of Computing, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia.
  • Farzana Kabir Ahmad School of Computing, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia.
  • Mohamad Farhan Mohamad Mohsen School of Computing, Universiti Utara Malaysia, 06010 UUM Sintok, Kedah, Malaysia.
  • Ahmad Kadri Junoh Institute of Engineering Mathematics, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia.
  • Mo'ath Allwaise Institute of Engineering Mathematics, Universiti Malaysia Perlis, 02600 Arau, Perlis, Malaysia.

Keywords:

Classification, Conditional Correlation, Multi Label Classification, Unconditional Correlations,

Abstract

In the last few years, multi label classification has attracted many scholars and researchers; due to the increasing number of modern domains that are applicable to this general type of classification. Recently, it has been believed by many researchers that the best way to handle the problem of multi label classification is by exploiting the correlations among labels. Two main strategies have been utilized to capture these correlations: conditional correlations and unconditional correlations capturing strategies. In this paper, an extensive evaluation of both strategies has been conducted, to determine the best strategy to handle multi label classification, with respect to the size of the data set and the optimized loss function. Results showed that the unconditional correlations capturing strategy overcomes the conditional correlations capturing strategy in all multi label data sets that have been used in this experiment.

References

S. Bengio, J. Weston, and D. Grangier, "Label embedding trees for large multi-class tasks," in Proc. Advances in Neural Information Processing Systems, Vancouver, BC, Canada , 2010, pp. 163-171.

K. Dembczynski, W. Waegeman, W. Cheng, and E. Hullermeier, "On label dependence and loss minimization in multi-label classification," Machine Learning, vol. 88, no. 1, pp. 5-45, 2012.

R. Alazaidah and F. Ahmad, "Trending Challenges in Multi Label Classification," International Journal of Advanced Computer Science and Applications, vol. 7, no. 10, pp. 127-131, 2016.

R. Alazaidah, F. Thabtah, and Q. Al-Radaideh, "A Multi-Label Classification Approach Based on Correlations Among Labels," International Journal of Advanced Computer Science and Applications, vol. 6, no. 2, pp. 52-59, 2015.

E. Gibaja, and S. Ventura, "A Tutorial on Multilabel Learning," ACM Computing Surveys (CSUR), vol. 47, no. 3, Apr., 2015.

G. Tsoumakas, and I. Vlahavas, "Random k-labelsets: An Ensemble Method for Multilabel Classification," in Proc. 18th European Conference on Machine Learning, Warsaw, Poland, 2007, pp. 406– 417.

L. Rokach, A. Schclar, and E. Itach, "Ensemble methods for multi-label classification," Expert Systems with Applications, vol. 41, no. 16, pp. 7507-7523, 2014.

J. Read, B. Pfahringer, G. Holmes, and E. Frank, "Classifier chains for multi-label classification," Machine Learning, vol. 85, no. 3, pp. 333- 359, 2011.

W. Cheng, E. Hüllermeier, and K. J. Dembczynski, "Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains," in Proc. 27th International Conference on Machine Learning (ICML-10), Haifa, 2010, pp. 279–286.

J. Read, "A pruned problem transformation method for multi-label classification," in Proc. 2008 New Zealand Computer Science Research Student Conference (NZCSRS 2008), New Zealand, 2008, vol. 143150.

I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large Margin Methods for Structured and Interdependent Output Variables," Journal of Machine Learning Research, vol. 6, pp. 1453-1484, 2005.

A. Elisseeff and J. Weston, "A kernel method for multi-labelled classification,", in Proc. Advances in Neural Information Processing Systems, Vancouver, BC, Canada , 2001, pp. 681-687.

J. Fürnkranz, E. Hüllermeier, E. Loza Mencía, and K. Brinker, "Multilabel classification via calibrated label ranking," Machine Learning, vol. 73, no. 2, pp. 133-153, 2008.

M. L. Zhang, and K. Zhang, "Multi-label learning by exploiting label dependency," in Proc. the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, Washington, DC, 2010, pp. 999–1008.

M. L. Zhang, and Z. H. Zhou, "ML-KNN: A lazy learning approach to multi-label learning," Pattern Recognition, vol. 40, no. 7, pp. 2038– 2048, 2007.

Y. Guo, and S. Gu, "Multi-label Classification using Conditional Dependency Networks," in Proc. The Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI’11), Barcelona, Spain, 2011, pp. 1300-1305.

W. Cheng, and E. Hüllermeier, "Combining instance-based learning and logistic regression for multilabel classification,". Machine Learning, vol. 76, no. 2, pp. 211-225, 2009.

Downloads

Published

2018-07-03

How to Cite

Alazaidah, R., Ahmad, F. K., Mohamad Mohsen, M. F., Junoh, A. K., & Allwaise, M. (2018). Evaluating Conditional and Unconditional Correlations Capturing Strategies in Multi Label Classification. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(2-4), 47–51. Retrieved from https://jtec.utem.edu.my/jtec/article/view/4315