Ändra sökning
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Applied machine learning in the logistics sector: A comparative analysis of supervised learning algorithms
Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för programvaruteknik.
2018 (Engelska)Självständigt arbete på grundnivå (kandidatexamen), 10 poäng / 15 hpStudentuppsats (Examensarbete)
Abstract [en]

BackgroundMachine learning is an area that is being explored with great haste these days, which inspired this study to investigate how seven different supervised learning algorithms perform compared to each other. These algorithms were used to perform classification tasks on logistics consignments, the classification is binary and a consignment can either be classified as missed or not.

ObjectivesThe goal was to find which of these algorithms perform well when used for this classification task and to see how the results varied with different sized datasets. Importance of the features which were included in the datasets has been analyzed with the intention of finding if there is any connection between human errors and these missed consignments.

MethodsThe process from raw data to a predicted classification has many steps including data gathering, data preparation, feature investigation and more. Through cross-validation, the algorithms were all trained and tested upon the same datasets and then evaluated based on the metrics recall and accuracy.

ResultsThe scores on both metrics increase with the size of the datasets, and when comparing the seven algorithms, two does not perform equally compared to the other five, which all perform moderately the same.

Conclusions Any of the five algorithms mentioned prior can be chosen for this type of classification, or to further study based on other measurements, and there is an indication that human errors could play a part on whether a consignment gets classified as missed or not.

Ort, förlag, år, upplaga, sidor
2018. , s. 29
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:bth-16656OAI: oai:DiVA.org:bth-16656DiVA, id: diva2:1228842
Externt samarbete
Wireless Independent Provider
Ämne / kurs
DV1478 Kandidatarbete i datavetenskap
Utbildningsprogram
DVGIS IT-säkerhet
Presentation
2018-05-30, 08:30 (Engelska)
Handledare
Examinatorer
Tillgänglig från: 2018-07-03 Skapad: 2018-06-28 Senast uppdaterad: 2018-07-03Bibliografiskt granskad

Open Access i DiVA

fulltext(471 kB)108 nedladdningar
Filinformation
Filnamn FULLTEXT02.pdfFilstorlek 471 kBChecksumma SHA-512
7771e39da15a9dcad822dbf0df6316c783272012a487bf2478a77c8edfbef5463a37f12601aa7ae50c8c15ba9b630f41e04b44b595f838f8b12650a951bb44ba
Typ fulltextMimetyp application/pdf

Av organisationen
Institutionen för programvaruteknik
Datavetenskap (datalogi)

Sök vidare utanför DiVA

GoogleGoogle Scholar
Totalt: 108 nedladdningar
Antalet nedladdningar är summan av nedladdningar för alla fulltexter. Det kan inkludera t.ex tidigare versioner som nu inte längre är tillgängliga.

urn-nbn

Altmetricpoäng

urn-nbn
Totalt: 645 träffar
RefereraExporteraLänk till posten
Permanent länk

Direktlänk
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annat format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annat språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf