|本期目录/Table of Contents|

[1]杨力,陆红娟,张鑫,等.多类支持向量机在煤矿安全评价中的应用研究[J].中国安全生产科学技术,2012,8(4):111.
 YANG Li,LU Hong juan,ZHANG Xin,et al.Application research of multiclass support vector machines in coal mine safety evaluation[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2012,8(4):111.
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多类支持向量机在煤矿安全评价中的应用研究
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
8
期数:
2012年4期
页码:
111
栏目:
出版日期:
2012-04-30

文章信息/Info

Title:
Application research of multiclass support vector machines in coal mine safety evaluation
作者:
杨力陆红娟张鑫盛武
(安徽理工大学 经济与管理学院, 淮南〓232001)
Author(s):
YANG LiLU HongjuanZHANG XinSHENG Wu
(School of Economics & Management, Anhui University of Science and Technology,Huainan 232001, China)
关键词:
支持向量机多分类煤矿安全评价
Keywords:
support vector machinemulticlass classificationcoal mine safety evaluation
分类号:
X913.4
DOI:
-
文献标志码:
A
摘要:
针对矿井子系统诸多、环境复杂、影响因素多变和在现实条件下难以获得大量煤矿样本的情况,提出将对非线性、小样本问题有较高处理能力的支持向量机理论引入到机制评价中,并在归纳了支持向量分类机从一对多到一对一再到决策树模式的多渠道多层次分类原理基础上,建立了基于多分类支持向量机原理的煤矿安全多层次评价模型,同时通过提取影响煤矿安全因素的特征参数,引入类权重因子和样本权重因子,较好地解决了训练样本类别数量不平衡、数据干扰导致的错分问题,实现了对煤矿安全较高准确率和较高效率的评价。
Abstract:
Given the situation that there are many mine subsystems with varied impact factors under complex environment, and the difficulty to obtain a large number of coal samples, the support vector machine theory was introduced into the evaluation mechanism. The SVM have a higher processing capacity for nonlinear or small sample problems than others. On the basis of summarizing the principle of SVM multiclassification from 1ar to 1a1 to Decision tree model, establish a multilevel model for coalmine safety evaluation in this paper. Introduce weighting factor and samples weighting factor by extracting characteristic parameters of factors that affect coal mine safety, solving the imbalance in the number of training samples and the data type of interference caused by the wrong subproblems, to achieve a higher accuracy rate for coal mine safety and efficiency evaluation.

参考文献/References:

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备注/Memo

备注/Memo:
国家自然科学基金项目(编号:71071003);教育部人文社会科学研究青年基金项目(编号:09YJC630004);安徽省高等学校省级重点自然科学项目(编号:KJ2009A59,kj2011A090)
更新日期/Last Update: 2012-05-07