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Machine learning in logistics: Increasing the performance of machine learning algorithms on two specific logistic problems
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2017 (English)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesisAlternative title
Maskininlärning i logistik : Öka prestandan av maskininlärningsalgoritmer på två specifika logistikproblem. (Swedish)
Abstract [en]

Data Ductus, a multination IT-consulting company, wants to develop an AI that monitors a logistic system and looks for errors. Once trained enough, this AI will suggest a correction and automatically right issues if they arise.

This project presents how one works with machine learning problems and provides a deeper insight into how cross-validation and regularisation, among other techniques, are used to improve the performance of machine learning algorithms on the defined problem. Three techniques are tested and evaluated in our logistic system on three different machine learning algorithms, namely Naïve Bayes, Logistic Regression and Random Forest.

The evaluation of the algorithms leads us to conclude that Random Forest, using cross-validated parameters, gives the best performance on our specific problems, with the other two falling behind in each tested category. It became clear to us that cross-validation is a simple, yet powerful tool for increasing the performance of machine learning algorithms.

Abstract [sv]

Data Ductus, ett multinationellt IT-konsultföretag vill utveckla en AI som övervakar ett logistiksystem och uppmärksammar fel. När denna AI är tillräckligt upplärd ska den föreslå korrigering eller automatiskt korrigera problem som uppstår.

Detta projekt presenterar hur man arbetar med maskininlärningsproblem och ger en djupare inblick i hur kors-validering och regularisering, bland andra tekniker, används för att förbättra prestandan av maskininlärningsalgoritmer på det definierade problemet. Dessa tekniker testas och utvärderas i vårt logistiksystem på tre olika maskininlärnings algoritmer, nämligen Naïve Bayes, Logistic Regression och Random Forest.

Utvärderingen av algoritmerna leder oss till att slutsatsen är att Random Forest, som använder korsvaliderade parametrar, ger bästa prestanda på våra specifika problem, medan de andra två faller bakom i varje testad kategori. Det blev klart för oss att kors-validering är ett enkelt, men kraftfullt verktyg för att öka prestanda hos maskininlärningsalgoritmer.

Place, publisher, year, edition, pages
2017. , p. 36
Keywords [en]
Machine learning, confusion matrix, performance, random forest, naïve bayes, logistic regression, cross-validation, regularisation
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ltu:diva-64761OAI: oai:DiVA.org:ltu-64761DiVA, id: diva2:1119345
Educational program
Computer Game Programming, bachelor's level
Available from: 2017-08-07 Created: 2017-07-03 Last updated: 2018-01-13Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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