In analysing sensor data, it usually proves beneficial to use domain knowledge in the classification process in order to narrow down the search space of relevant features. However, it is often not effective when decision trees or the k-NN method is used. Therefore, the authors herein propose to build an appropriate concept ontology based on expert knowledge. The use of an ontology-based metric enables mutual similarity to be determined between objects covered by respective concept ontology, taking into consideration interrelations of features at various levels of abstraction. Using a set of medical data collected with the Holter method, it is shown that predicting coronary disease with the use of the approach proposed is much more accurate than in the case of not only the k-NN method using classical metrics, but also most other known classifiers. It is also proved in this paper that the expert determination of appropriate structure of ontology is of key importance, while subsequent selection of appropriate weights can be automated.

中文翻译：

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概念本体支持的k-NN方法有效构造分类器

在分析传感器数据时，通常证明在分类过程中使用领域知识以缩小相关特征的搜索空间是有益的。但是，当使用决策树或k-NN方法时，它通常无效。因此，本文的作者提议基于专家知识来构建适当的概念本体。基于本体的度量的使用使得能够确定各个概念本体所覆盖的对象之间的相互相似性，同时考虑到各种抽象级别的特征的相互关系。使用通过Holter方法收集的一组医学数据，结果表明，与不仅使用经典指标的k-NN方法而且大多数其他方法相比，使用建议的方法预测冠状动脉疾病的准确性要高得多。已知的分类器。