Learning Probabilistic Relational Concepts

本研究的目的是研究从嘈杂的关系数据中学习概率关系概念的算法的效用，重点研究使用证据组合进行分类的算法。特别令人感兴趣的是，如果考虑到规则的可靠性，可以获得分类准确性的潜在改进。另一个目标是扩展耐噪声学习算法，以利用待学习概念的不完整或不确定知识。这些知识可能被表示为部分不正确或不完整的领域理论。这些算法将在UCI机器学习数据库中的许多数据集上进行测试，并在涉及脊柱损伤识别、矿物光谱识别和溢油分析的三个新的关系数据集上进行测试。此外，它们将在许多合成域上进行测试，其中参数的变化将允许深入了解这些算法的一般行为。HYDRA系统（Ali & Pazzani, 1993）已经在有限数量的数据集上显示出一些希望，将在这些调查中得到显著扩展。

9310413 Pazzani The objective of the proposed research are to investigate the utility of algorithms that learn probabilistic relational concepts from noisy, relational data, concentrating on algorithms that perform classification using evidence combination. Of particular interest is the potential improvement in classification accuracy that can be obtained if the reliability of rules is taken into account. An additional objective is to extend noise-tolerant learning algorithms to take advantage of incomplete or uncertain knowledge of the concept to be learned. Such knowledge may be expressed as a partially incorrect or incomplete domain theory. These algorithms will be tested on a number of data sets from the UCI Repository of Machine Learning data sets and on three new relational data sets involving spinal injury identification, mineral spectra identification, and oil spill analysis. In addition, they will be tested on a number of synthetic domains, where variation of parameters will allow insight into the general behavior of these algorithms. The system HYDRA (Ali & Pazzani, 1993), which has already shown some promise on a limited number of data sets will be significantly extended for these investigations.