Operational Patterns Recognition Using Multi-class Cost-sensitive Learning for Alumina Evaporation Process

TANG Mingzhu; YANG Chunhua; QING Jingjing; GUI Weihua

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(2): 282-286

TANG Mingzhu, YANG Chunhua, QING Jingjing, et al., “Operational Patterns Recognition Using Multi-class Cost-sensitive Learning for Alumina Evaporation Process,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 282-286, 2013,

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TANG Mingzhu, YANG Chunhua, QING Jingjing, et al., “Operational Patterns Recognition Using Multi-class Cost-sensitive Learning for Alumina Evaporation Process,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 282-286, 2013,

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Operational Patterns Recognition Using Multi-class Cost-sensitive Learning for Alumina Evaporation Process

1.
School of Information Science and Engineering, Central South University, Changsha 410083, China;
2.
School of Energy and Power Engineering, Changsha University of Science and Engineering, Changsha 410007, China

Funds: This work is supported in part by the National Natural Science Foundation of China (No.60874069), the National Science Fund for Distinguished Young Scholars of China (No.61025015), and the Research Foundation of Education Bureau of Hunan Province, China (No.12A007).

Received Date: 2011-08-01
Rev Recd Date: 2012-03-01
Publish Date: 2013-04-25

Abstract

Abstract

The characteristics of Alumina evaporating process (AEP) are analyzed firstly. Operational pattern is defined to describe the AEP. A new framework is proposed, which formulates the operational pattern recognition problem as a multi-class class-imbalanced problem of unequal misclassification costs. Aim to the multiclass class-imbalanced problem of unequal misclassification costs in the operational pattern set of AEP, a multiclass cost-sensitive Probabilistic neural network (mc-PNN) method is proposed. A spectral clustering based on Balanced iterative reducing and clustering using hierarchies (BIRCH) is used to optimize the number of pattern layer nodes of mc-PNN. Experimental results show that the proposed method reduces effectively average misclassification costs and increases recognition rate of excellent and faulty classes.
- Cost-sensitive probabilistic neural network,
- Operational pattern,
- Alumina evaporating process,
- Class-imbalanced problem,
- Unequal misclassification costs

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References(8)

References

N. Abe et al., "An iterative method for multi-class cost-sensitive learning", Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, pp.3-11, 2004.

M.A. Davenport, "The 2nu-SVM: A cost-sensitive extension of the nu-SVM", Technical Report, Rice University, Department of Electrical and Computer Engineering, TREE 0504, 2005.

S.W. Zhao et al., "A multi-objective optimization based constructing cost-sensitive decision trees method", Acta Electronica Sinica, Vol.39, pp.2348-2352, 2011. (in Chinese)

C.Y. Yang et al., "Margin calibration in SVM class-imbalanced learning", Neurocomputing, Vol.73, pp.397-411, 2009.

Y. Lee et al., "Multicategory support vector machines", Journal of the American Statistical Association, Vol.99, pp.67-81, 2004.

Z.H. Zhou and X.Y. Liu, "On multi-class cost-sensitive learning", Computational Intelligence, Vol.26, pp.232-257, 2010.

Y. Zhang and Z.H. Zhou, "Cost-sensitive face recognition", IEEE Transactions on Pattern Analysis and Machine Intelligence, pp.1758-1769, 2009.

M.Z. Tang et al., "Cost-sensitive probabilistic neural network with its application in fault diagnosis", Control and Decision, Vol.25, pp.1074-1078, 2010.

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