Zupan B, Dzeroski S
Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia.
Artif Intell Med. 1998 Sep-Oct;14(1-2):101-17. doi: 10.1016/s0933-3657(98)00018-9.
Domain or background knowledge is often needed in order to solve difficult problems of learning medical diagnostic rules. Earlier experiments have demonstrated the utility of background knowledge when learning rules for early diagnosis of rheumatic diseases. A particular form of background knowledge comprising typical co-occurrences of several groups of attributes was provided by a medical expert. This paper explores the possibility of automating the process of acquiring background knowledge of this kind and studies the utility of such methods in the problem domain of rheumatic diseases. A method based on function decomposition is proposed that identifies typical co-occurrences for a given set of attributes. The method is evaluated by comparing the typical co-occurrences it identifies as well as their contribution to the performance of machine learning algorithms, to the ones provided by a medical expert.
为了解决学习医学诊断规则的难题，通常需要领域或背景知识。早期实验已经证明了背景知识在学习风湿性疾病早期诊断规则时的效用。一位医学专家提供了一种特殊形式的背景知识，它包含几组属性的典型共现情况。本文探讨了自动获取此类背景知识过程的可能性，并研究了这些方法在风湿性疾病问题领域中的效用。提出了一种基于功能分解的方法，该方法可识别给定属性集的典型共现情况。通过将该方法识别出的典型共现情况及其对机器学习算法性能的贡献与医学专家提供的情况进行比较，对该方法进行了评估。
