Quality Inspection with Chi-Square Automatic Interaction Detector and Self-Organizing Map

Lelija Stupar; Quan Yu; Ke Sheng Wang

doi:10.4028/www.scientific.net/AMR.1039.538

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 1039Quality Inspection with Chi-Square Automatic...

Quality Inspection with Chi-Square Automatic Interaction Detector and Self-Organizing Map

Abstract:

This paper describes two methods for the industrial quality inspection: Supervised classification algorithm Chi-Square Automatic Interaction Detector (CHAID) and unsupervised clustering algorithm Self-Organizing Map (SOM). The classification and clustering are modelled in IBM software SPSS. Models’ functioning is illustrated on a wheel assembly geometric features inspection. The classifying accuracies are compared for the two methods. CHAID has shown better classifying ability than SOM, while SOM can be used to improve quality of predictor values, and therefore classifiers accuracy.

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Periodical:

Advanced Materials Research (Volume 1039)

Pages:

538-543

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1039.538

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Online since:

October 2014

Authors:

Lelija Stupar*, Quan Yu, Ke Sheng Wang

Keywords:

Attribute Selection Method, Automatic Interaction Detector, CHAID, Self-Organizing Map, SOM, SPSS, Supervised Machine Learning, Unsupervised Machine Learning

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