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Author (up) Ruz, G.A. pdf  doi
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  Title Improving the performance of inductive learning classifiers through the presentation order of the training patterns Type
  Year 2016 Publication Expert Systems With Applications Abbreviated Journal Expert Syst. Appl.  
  Volume 58 Issue Pages 1-9  
  Keywords Inductive learning; Rules family; Clustering; Classification  
  Abstract Although the development of new supervised learning algorithms for machine learning techniques are mostly oriented to improve the predictive power or classification accuracy, the capacity to understand how the classification process is carried out is of great interest for many applications in business and industry. Inductive learning algorithms, like the Rules family, induce semantically interpretable classification rules in the form of if-then rules. Although the effectiveness of the Rules family has been studied thoroughly and new and improved versions are constantly been developed, one important drawback is the effect of the presentation order of the training patterns which has not been studied in depth previously. In this paper this issue is addressed, first by studying empirically the effect of random presentation orders in the number of rules and the generalization power of the resulting classifier. Then a presentation order method for the training examples is proposed which combines a clustering stage with a new density measure developed specifically for this problem. The results using benchmark datasets and a real application of wood defect classification show the effectiveness of the proposed method. Also, since the presentation order method is employed as a preprocessing stage, the simplicity of the Rules family is not affected but instead it enables the generation of fewer and more accurate rules, which can have a direct impact in the performance and usefulness of the Rules family in an expert system context. (C) 2016 Elsevier Ltd. All rights reserved.  
  Address [Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Av Diagonal Las Torres 2640, Santiago, Chile, Email: gonzalo.ruz@uai.cl  
  Corporate Author Thesis  
  Publisher Pergamon-Elsevier Science Ltd Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0957-4174 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000376552600001 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 633  
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