TANG Mingzhu, YANG Chunhua, QING Jingjing, GUI Weihua. Operational Patterns Recognition Using Multi-class Cost-sensitive Learning for Alumina Evaporation Process[J]. Chinese Journal of Electronics, 2013, 22(2): 282-286.
Citation: TANG Mingzhu, YANG Chunhua, QING Jingjing, GUI Weihua. Operational Patterns Recognition Using Multi-class Cost-sensitive Learning for Alumina Evaporation Process[J]. Chinese Journal of Electronics, 2013, 22(2): 282-286.

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

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
  • 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.
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