摘要
为了解决电子商务环境中由于信息的保密性使协商参与者无法获得对手协商偏好从而影响协商性能的问题,提出一种基于分类器融合的自动化协商决策模型.该模型融合支持向量机和贝叶斯分类器,通过结合2种分类器的优点,提高对协商偏好的分类学习效果.在准确估计对手协商偏好的基础上,采用粒子群优化算法搜寻最优协商反建议.实验数据分析表明,新方法的效果优于单一分类器,并且在有噪声的小规模训练样本集下,仍然保持较高的协商总效用.
Due to the confidentiality of information in e-commerce environment, negotiation participants can not get opponent ;s negotiation preferences, thereby affecting the negotiation performance. To solve this, an automated negotiation decision model based on classifier fusion was proposed. The model incorporates support vector machine and Bayesian classifier by combining the advantages of both, improving the effect of classification learning of negotiation preferences. Based on accurate estimation of opponent＇s negotiation preference, a particle swarm optimization algorithm was used to search the optimal counter proposal. The experimental data show that the new method is better than the single classifier, and can maintain a high total negotiation utility in the noisy small scale training set.
出处
《上海交通大学学报》
EI
CAS
CSCD
北大核心
2013年第4期644-649,共6页
Journal of Shanghai Jiaotong University
基金
国家自然科学基金(61175058)
浙江省自然科学基金(Y1100036
LY12F02018)
浙江省自然科学基金(Z12F020019)资助项目
浙江省教育厅科研计划基金(Y201016929
Y201222997)
关键词
自动化协商
支持向量机
贝叶斯
automated negotiation
support vector machine
Bayesian
