Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/8C82A337-9129-46E6-BE01-43927766AE6E | - |
| Αναγνωριστικό | https://doi.org/10.1108/09544780510594207 | - |
| Γλώσσα | en | - |
| Τίτλος | Using machine learning to support quality management: Framework and experimental investigation | en |
| Δημιουργός | Moustakis Vasilis | en |
| Δημιουργός | Μουστακης Βασιλης | el |
| Δημιουργός | Tsironis Loukas | en |
| Δημιουργός | Τσιρωνης Λουκας | el |
| Δημιουργός | Bilalis Nikolaos | en |
| Δημιουργός | Μπιλαλης Νικολαος | el |
| Εκδότης | Emerald | en |
| Περίληψη | Purpose – To demonstrate the applicability of machine‐learning tools in quality management. Design/methodology/approach – Two popular machine‐learning approaches, decision tree induction and association rules mining, were applied on a set of 960 production case records. The accuracy of results was investigated using randomized experimentation and comprehensibility of rules was assessed by experts in the field. Findings – Both machine‐learning approaches exhibited very good accuracy of results (average error was about 9 percent); however, association rules mining outperformed decision tree induction in comprehensibility and correctness of learned rules. Research limitations/implications – The proposed methodology is limited with respect to case representation. Production cases are described via attribute‐value sets and the relation between attribute values cannot be determined by the selected machine‐learning methods. Practical implications – Results demonstrate that machine‐learning techniques may be effectively used to enhance quality management procedures and modeling of cause‐effect relationships, associated with faulty products. Originality/value – The article proposes a general methodology on how to use machine‐learning techniques to support quality management. The application of the technique in ISDN modem manufacturing demonstrates the effectiveness of the proposed general methodology. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-10-16 | - |
| Ημερομηνία Δημοσίευσης | 2005 | - |
| Βιβλιογραφική Αναφορά | Loukas Tsironis, Nikos Bilalis, Vassilis Moustakis, (2005) "Using machine learning to support quality management: Framework and experimental investigation", The TQM Magazine, Vol. 17 Iss: 3, pp.237 - 248, DOI: 10.1108/09544780510594207 | en |
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