A comparison between neural networks and decision trees based on data from industrial radiographic testing

被引：53

作者：

Perner, P

Zscherpel, U

Jacobsen, C

机构：

[1] Inst Comp Vis & Appl Comp Sci Leipzig, D-04277 Leipzig, Germany

[2] Bundesanstalt Mat Forsch & Prufung, D-12205 Berlin, Germany

来源：

PATTERN RECOGNITION LETTERS | 2001年 / 22卷 / 01期

关键词：

machine learning; decision tree induction; neural networks; performance analysis; radiographic testing; pipe inspection;

D O I：

10.1016/S0167-8655(00)00098-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we are empirically comparing the performance of neural nets and decision trees based on a data set for the detection of defects in welding seams. This data set was created by image feature extraction procedures working on digitized X-ray films. We introduce a framework for distinguishing classification methods. We found that more detailed analysis of the error rate is necessary in order to judge the performance of the learning and classification method. However, the error rate cannot be the only criterion for comparing between the different learning methods. This is a more complex selection process that involves more criteria that we are describing in this paper. (C) 2001 Elsevier Science B.V. All rights reserved.

引用

页码：47 / 54

页数：8