| 摘要: |
| 现有的关系学习研究都是基于完备数据进行的,而现实问题中,数据通常是不完备的.提出一种从不完备关系数据中学习概率关系模型(probabilistic relational models,简称PRMs)的方法——MLTEC(maximum likelihood tree and evolutionary computing method).首先,随机填充不完备关系数据得到完备关系数据.然后从每个随机填充后的数据样本中分别生成最大似然树并作为初始PRM网络,再利用进化过程中最好的网络结构反复修正不完备数据集,最 |
| 关键词: 机器学习 关系学习 不完备数据 概率关系模型 最大似然树 进化计算 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant Nos.60635030, 60473046 (国家自然科学基金); the China Postdoctoral Science Foundation under Grant No.20060390921 (中国博士后科学基金); the Jiangsu Planned Projects for Postdoctoral Research Funds of |
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| An Approach to Learning PRM from Incomplete Relational Data |
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LI Xiao-Lin,ZHOU Zhi-Hua
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| Abstract: |
| Existing relational learning approaches usually work on complete relational data. However, in real-world applications, data are often incomplete. This paper proposes the MLTEC (maximum likelihood tree and evolutionary computing method) method to learn structures of the probabilistic relational models (PRMs) from incomplete relational data. The incomplete relational data are filled randomly at first, and a maximum likelihood tree (MLT) is generated from each completed data sample. This population of MLTs is then evolved through an evolutionary computing process, and the incomplete data are modified by using the best evolved structure in each generation. As a result, the probabilistic structure is learned. Experimental results show that the MLTEC method can learn good structures from incomplete relational data. |
| Key words: machine learning relational learning incomplete data probabilistic relational model maximum likelihood tree evolutionary computing |
