社会计算中的组织行为建模研究
文献类型：学位论文
| 作者 | 苏鹏 |
| 学位类别 | 工学博士 |
| 答辩日期 | 2011-05-29 |
| 授予单位 | 中国科学院研究生院 |
| 授予地点 | 中国科学院自动化研究所 |
| 导师 | 曾大军 |
| 关键词 | 组织行为建模 组织行为预测 可操作行为规则 代价敏感学习 类不平衡问题 Organizational behavior modeling Organizational behavior prediction Actionable behavioral rules Cost-sensitive learning Class imbalance problem |
| 其他题名 | Modeling Organizational Behavior: A Social Computing Approach |
| 学位专业 | 计算机应用技术 |
| 中文摘要 | 近年来，国际国内各种社会组织的数量及活跃度快速增长，对各国的政治、经济等领域的影响日益加深。因此，迫切需要研究各种社会组织的行为规律，为政府等利益主体的科学决策提供坚实依据。随着互联网等新技术的飞速发展，信息变得空前丰富并易于存取，使得高效的计算技术在组织行为建模领域日益表现出巨大优势，并逐渐成为组织行为建模的主要方法。 目前，社会组织行为建模研究主要集中在构建预测模型以预测组织可能的行为。机器学习方法，特别是分类方法，近年来成为了组织行为预测建模的主要方法。本文比较分析了主要的分类方法所建立的组织行为预测模型的性能，为不同情形下分类方法的恰当选择提供了依据。组织行为数据普遍存在类不平衡和误分类代价不一致问题，这导致标准分类器所构建的组织行为预测模型性能较差。为此，在期望误分类代价这一指标下，本文经验研究了四种典型代价敏感学习方法基于不同标准分类器所构建的组织行为预测模型的性能，为不同情形下代价敏感学习方法的恰当选择提供了依据。另外，提出了一个新的适用于本领域的代价敏感学习算法。实验结果表明该算法取得了比其它五种常见代价敏感学习方法更优的性能。最后，针对本领域误分类代价易变且不易确定等特点，提出了一个基于代价曲线的个性化解决方案。该方案可使用户方便直观的为给定数据集选择最优代价敏感学习方法-分类器组合。 尽管组织行为预测模型可提供相当准确的组织行为预测知识，但却不能提供可被用户直接用来影响（抑制或鼓励）组织行为并因此获益的具体行动建议。这些行动建议又称为可操作知识，常常是用户所切实需要的。可操作性是知识兴趣度的重要方面，使挖掘出的模式具有可操作性是数据挖掘的中心主题之一。然而，尽管已有不少研究致力于其它类型的可操作知识发现，挖掘影响组织行为的可操作规则（可操作行为规则）这一重要问题尚未被识别、定义和研究。为此，本文建立了一类新的组织行为建模问题——基于可操作行为规则挖掘的组织行为建模。具体来说，本文提出了可操作行为规则挖掘问题的形式化定义，并提出两个可靠有效的可操作行为规则挖掘算法。值得强调的是，可操作行为规则挖掘也可应用于国家、群体、个人等其它实体的行为建模。另外，本文提出了可操作行为规则挖掘算法（模型）的验证方法，填补了可操作规则挖掘领域的空白。 |
| 英文摘要 | In recent years, the number and activeness of various national and international social organizations have increased rapidly. They have had increasingly profound impact on the political, economic and other situations worldwide. Consequently, there is great demand for studying the behavioral patterns of social organizations to facilitate the decision making processes of related stakeholders, such as governments. With the rapid development of new technologies such as the Internet, information has become unprecedentedly rich and easy to access. As a result, efficient computing technologies have demonstrated great advantage on modeling organizational behavior, and become the main research trends. Thus, modeling organizational behavior has become one of the major themes in social computing. At present, research on modeling organizational behavior has focused on building predictive models using machine learning methods, in particular classification methods. In this dissertation, we first evaluate the predictive models constructed by seven representative classification algorithms and gain a thorough insight into the pros and cons of each algorithm. Secondly, we found that in organizational behavior data, class imbalance and non-uniform misclassification costs are pervasive in this domain, which severely hinder the performance of standard classifiers. To handle this problem, we empirically investigate four typical cost-sensitive learning methods, combined with six standard classifiers. Our empirical study verifies the effectiveness of cost-sensitive learning in organizational behavior modeling. Based on the experimental results, we gain a thorough insight into the problem of class imbalance and non-uniform misclassification costs, as well as the selection of cost-sensitive methods, base classifiers and method-classifier pairs for this domain. We also propose an improved algorithm which outperforms the best method-classifier pair using the benchmark organizational behavior data. In addition, we further propose a personalized solution based on cost curves. Given a dataset, this solution makes it very easy for the user to select the best method-classifier pair. Although predictive models provide accurate predictions of organizational behavior, they do not directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior for the user’s interest. The user, however, often exactly needs such kind of actionable knowledge. Acti... |
| 语种 | 中文 |
| 其他标识符 | 200818014628057 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/6366]  |
| 专题 | 毕业生_博士学位论文 |
| 推荐引用方式 GB/T 7714 | 苏鹏. 社会计算中的组织行为建模研究[D]. 中国科学院自动化研究所. 中国科学院研究生院. 2011. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。