Excellent operational pattern recognition based on simultaneously optimizing cost-sensitive support vector machine

doi:10.3969/j.issn.0438-1157.2013.12.036

›› 2013, Vol. 64 ›› Issue (12): 4509-4514.DOI: 10.3969/j.issn.0438-1157.2013.12.036

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Excellent operational pattern recognition based on simultaneously optimizing cost-sensitive support vector machine

TANG Mingzhu^1,2, YANG Chunhua³

1. School of Energy and Power Engineering, Changsha University of Science & Technology, Changsha 410007, Hunan, China;
2. Hunan Province University Key Laboratory of Bridge Engineering (Changsha University of Science & Technology), Changsha 410007, Hunan, China;
3. School of Information Science and Engineering, Central South University, Changsha 410083, Hunan, China

Received:2013-08-16 Revised:2013-08-28 Online:2013-12-05 Published:2013-12-05
Supported by:
supported by the National Science Fund for Distinguished Young Scholars of China (61025015)，the Research Foundation of Education Bureau of Hunan Province，China (12A007)，the Hunan Province University Key Laboratory of Bridge Engineering and the Introduction of Talent Fund of Changsha University of Science & Technology.

基于同步优化的代价敏感支持向量机优良类操作模式识别

唐明珠^1,2, 阳春华³

1. 长沙理工大学能源与动力工程学院, 湖南长沙 410007;
2. 桥梁工程湖南省高校重点实验室(长沙理工大学), 湖南长沙 410007;
3. 中南大学信息科学与工程学院, 湖南长沙 410083

通讯作者: 唐明珠
作者简介:唐明珠（1983- ），男，博士，讲师。
基金资助:
国家杰出青年科学基金项目（61025015）；湖南省教育厅重点项目（12A007）；桥梁工程湖南省高校重点实验室重点项目；长沙理工大学人才引进基金项目。

Abstract

Abstract: Aiming at class-imbalanced operational pattern recognition and noise features for alumina evaporation process (AEP),a simultaneously optimizing cost-sensitive support vector machine (CSVM) is proposed in this paper.Studying the mechanism of AEP,input conditions,operating parameters and state parameters are selected as original operational pattern.The feature set of original operational pattern are optimized by the binary particle swarm optimization and the optimal feature subset is selected as the operational pattern.Meanwhile,the sigma of Gaussian kernel and misclassification cost parameters for CSVM are optimized by the linear weight diminishing particle swarm optimization.The proposed method is applied on the operational pattern optimization of AEP.Experimental results illustrate that the proposed method increases excellent operational pattern recognition rates and reduces misclassification costs.

Key words: cost-sensitive support vector machine, operational pattern, feature selection, experimental validation, alumina, algorithm

摘要： 针对氧化铝蒸发过程操作模式集中类别不平衡和噪声特征问题，提出基于同步优化的代价敏感支持向量机操作模式识别方法。对氧化铝蒸发过程机理进行分析，该过程的输入条件、操作参数和状态参数被选为原始操作模式，利用离散的粒子群算法优化操作模式的特征集，选择最优特征子集作为最终的操作模式；同时利用连续的粒子群算法优化代价敏感支持向量机的核参数和误分类代价参数，自动搜索和确定最优的核参数和误分类代价参数。工业应用结果表明，与粒子群优化操作模式特征子集或粒子群优化核参数和误分类代价参数相比，所提出的方法优良类操作模式识别高，误分类代价低。

关键词: 代价敏感支持向量机, 操作模式, 特征选择, 实验验证, 氧化铝, 算法

CLC Number:

TP18

TANG Mingzhu, YANG Chunhua. Excellent operational pattern recognition based on simultaneously optimizing cost-sensitive support vector machine[J]. , 2013, 64(12): 4509-4514.

唐明珠, 阳春华. 基于同步优化的代价敏感支持向量机优良类操作模式识别[J]. CIESC Journal, 2013, 64(12): 4509-4514.

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Excellent operational pattern recognition based on simultaneously optimizing cost-sensitive support vector machine

基于同步优化的代价敏感支持向量机优良类操作模式识别

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