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Machine Learning Methods for Fault Classification
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Maskininlärningsmetoder för felklassificering (Swedish)
Abstract [en]

This project, conducted at Ericsson AB, investigates the feasibility of implementing machine learning techniques in order to classify dump files for more effi cient trouble report routing. The project focuses on supervised machine learning methods and in particular Bayesian statistics. It shows that a program utilizing Bayesian methods can achieve well above random prediction accuracy. It is therefore concluded that machine learning methods may indeed become a viable alternative to human classification of trouble reports in the near future.

Abstract [sv]

Detta examensarbete, utfört på Ericsson AB, ämnar att undersöka huruvida maskininlärningstekniker kan användas för att klassificera dumpfiler för mer effektiv problemidentifiering. Projektet fokuserar på övervakad inlärning och då speciellt Bayesiansk klassificering. Arbetet visar att ett program som utnyttjar Bayesiansk klassificering kan uppnå en noggrannhet väl över slumpen. Arbetet indikerar att maskininlärningstekniker mycket väl kan komma att bli användbara alternativ till mänsklig klassificering av dumpfiler i en nära framtid.

Place, publisher, year, edition, pages
2014. , 48 p.
Keyword [en]
ai, A.I., Machine Learning, Fault Classification, Bayes, Classification., Computer Science
Keyword [sv]
Maskininlärningsmetoder, Maskininlärning, felklassificering, ai, A.I.
National Category
Computer Science
URN: urn:nbn:se:kth:diva-183132OAI: diva2:908072
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Available from: 2016-03-02 Created: 2016-03-01 Last updated: 2016-03-02Bibliographically approved

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