基于半监督吸引子传播聚类的上市公司绩效评价研究

项目名称： 基于半监督吸引子传播聚类的上市公司绩效评价研究

项目编号： No.61202306

项目类型： 青年科学基金项目

立项/批准年度： 2013

项目学科： 计算机科学学科

项目作者： 王丽敏

作者单位： 吉林财经大学

项目金额： 23万元

中文摘要： 上市公司绩效评价是政府、金融监管机构研究的热点，在产业结构调整、防范和规避金融风险等方面具有重要意义。近年来，半监督学习和吸引子传播聚类逐渐兴起。为此，本项目主要研究基于半监督吸引子传播聚类的上市公司绩效评价方法。其主要内容包括：（1）针对提高先验知识样本的学习效果和效率问题，构建指标赋权算子、距离差异最大化原则等策略，提出多群体协同智能赋权相似性度量方法。（2）构建主辅空间，类内、类外损失等策略，提出吸引子传播聚类新算法。（3）提出融合多种策略的半监督吸引子传播聚类模型和算法，并将其用于上市公司绩效评价。（4）仿真模拟与验证研究，与已有评价方法比较，最终形成一个较为完善的、实用性较强的上市公司绩效评价系统。项目研究成果将为上市公司评价、数据挖掘、信息智能化处理以及金融管理与投资决策等领域提供更加有效的方法和技术手段。

中文关键词： 半监督学习；吸引子传播聚类；群体智能算法；上市公司绩效评价；

英文摘要： The listed companies performance evaluation is the research focus for the government and organizations of financial supervision and management. It has a vital significance in the fields of the industrial structure adjustment and guard, avoiding the financial risk etc. In recent years, semi-supervised learning and affinity propagation clustering are emerging gradually. Therefore, the project focus on researching the method based on semi-supervised affinity propagation clustering which is used in the listed companies performance evaluation. It mainly including: (1)Aim at improving the learning effect and efficiency of prior knowledge samples, the strategies of index weighting operator and the distance difference maximization principle are to be constructed, so the novel similarity measure method based on swarm coordination intelligence weighting is to be proposed in the project.(2)The new efficient affinity propagation clustering algorithm is proposed by establishing the main space and auxiliary space, inside and outside sort loss strategies.(3)The new model and algorithm of semi-supervised learning and affinity propagation clustering are also proposed by integrating many approaches, and they are applied in the field of listed companies evaluation. (4)Simulation and validation research, comparison is also done wi

英文关键词： Semi-supervised Learning；Affinity Propagation Clustering；Swarm Intelligent Algorithm；Listed Companies Performance Evaluation；