基于事例的推理中相似事例选择的神经网络方法

A Neural Network Method of Similar Case Retrieval in Case-based Reasoning

  • 摘要: 在基于事例的推理(CBR)系统中,为解决采用最相邻近法选择相似事例时属性权值不易确定的问题,提出事例的前提相似度和结论相似度的概念,并采用一种人工神经网络结构进行属性权值的学习.该方法从事例库中自动获取属性权值,并用最相邻近法和学习到的属性权值进行相似事例选择.最后,在光动力治疗(PDT)鲜红斑痣(PWS)的临床病例库中应用该方法进行了试验.

     

    Abstract: In order to overcome the handicap of obtaining case attribute weights in classical nearest-neighbor approach to retrieve similar cases in case-based reasoning(CBR),the similarity of precondition and the similarity of conclusion are defined,and a neural network approach is used to learn the case attribute weights.In this method,the case attribute weights are obtained automatically from the case library.Using the method,the case attribute weights are attained from cases.The similar cases are retrieved by nearestneighbor classification with the learned case attribute weights.Finally,the approach is used in photodynamic therapy(PDT) Port Wine Stain(PWS) medicinal database.

     

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