[1]季瑞瑞,刘?? 丁.支持向量数据描述的基因表达数据聚类方法[J].智能系统学报,2009,4(6):544-548.[doi:10.3969/j.issn.1673-4785.2009.06.013]
JI Rui-rui,LIU Ding.Improved gene expression data clustering using a support vector domain description algorithm[J].CAAI Transactions on Intelligent Systems,2009,4(6):544-548.[doi:10.3969/j.issn.1673-4785.2009.06.013]
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JI Rui-rui,LIU Ding.Improved gene expression data clustering using a support vector domain description algorithm[J].CAAI Transactions on Intelligent Systems,2009,4(6):544-548.[doi:10.3969/j.issn.1673-4785.2009.06.013]
支持向量数据描述的基因表达数据聚类方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
4
期数:
2009年第6期
页码:
544-548
栏目:
学术论文—机器学习
出版日期:
2009-12-25
- Title:
- Improved gene expression data clustering using a support vector domain description algorithm
- 文章编号:
- 1673-4785(2009)06-0544-05
- Keywords:
- gene expression data; SVDD; clustering; simulated annealing
- 分类号:
- TP18
- 文献标志码:
- A
- 摘要:
- 为改善传统的基因表达数据聚类方法正确率偏低的问题，研究了支持向量数据描述（SVDD）算法在基因表达数据聚类中的应用，该方法通过寻找最优分类超球实现对数据集的有效聚类.将类间信息融入聚类有效性评估准则中，通过模拟退火优化算法寻找SVDD算法中的最优核函数参数和惩罚因子，在训练时引入非样本数据提高运算效率.对酵母细胞生长周期的基因表达数据集的仿真实验结果表明，在新的聚类有效性评估准则下进行参数寻优，能够更快更好地得到最佳参数，同时，算法具有聚类精度高和运算速度快的优点.
- Abstract:
- The application of the support vector domain description (SVDD) algorithm in gene expression data clustering was proposed as a means to improve the low accuracy of current clustering methods. This method effectivly clustered the dataset by finding the optimal separating hyper-sphere. Interclass information was introduced into the current clustering assessment criterion in the form of a minimum within-class distance. The simulated annealing (SA) algorithm was used to find the optimal kernel function parameter and the punishment factor of the SVDD algorithm. Non-sample data were added in training to increase computational efficiency. Simulation results using the yeast cell cycle expression dataset showed that optimal parameters can be obtained faster and more accurately with the new assessment criteria. Similar improvements were found in clustering accuracy and speed.
- 参考文献/References:
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备注/Memo
收稿日期：2009-06-23.
通信作者：季瑞瑞.Email: hezirui423@163.com.
作者简介：
季瑞瑞，女，1978年生，讲师，博士研究生，主要研究方向为人工智能与模式识别、生物信息处理与建模.发表学术论文6篇，其中被EI检索3篇.
刘?? 丁，男，1957年生，教授，博士生导师，中国人工智能学会常务理事，主要研究方向为复杂系统的建模与控制、智能机器人、系统控制等.发表学术论文100余篇，其中被SCI、EI检索50余篇.
更新日期/Last Update:
2010-02-22