Subjective matching of products using Machine Learning
(2017) In LU-CS-EX 2017-29 EDA920 20171Department of Computer Science
- Abstract (Swedish)
- When insurance companies calculate compensation for products no longer on the market, they need to identify an equal product for val- uation. Subjectively matching products poses a unique challenge with a many-to-many pairing, which is currently handled by an algorithm that requires day-to-day maintenance. This thesis investigates current methodologies and tools and presents unique machine learning meth- ods in an attempt to improve results and eliminate manual labor. By combining a multilayered neural network with cosine proximity com- parison, this thesis shows promising results in that a fully automated machine learning model can be implemented and replace the systems currently in use.
- Popular Abstract
- Insurance companies manually find replacements for thousands of products every day. Can a machine replace the manual labor while remaining faithful to the consumers’ desires?
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8928874
- author
- Åström, Joacim LU and Ekdahl, Alexander
- supervisor
- organization
- course
- EDA920 20171
- year
- 2017
- type
- H3 - Professional qualifications (4 Years - )
- subject
- keywords
- Machine learning, closest match, neural network, data processing
- publication/series
- LU-CS-EX 2017-29
- report number
- LU-CS-EX 2017-29
- ISSN
- 1650-2884
- language
- English
- id
- 8928874
- date added to LUP
- 2017-12-06 12:40:35
- date last changed
- 2017-12-06 12:40:35
@misc{8928874, abstract = {{When insurance companies calculate compensation for products no longer on the market, they need to identify an equal product for val- uation. Subjectively matching products poses a unique challenge with a many-to-many pairing, which is currently handled by an algorithm that requires day-to-day maintenance. This thesis investigates current methodologies and tools and presents unique machine learning meth- ods in an attempt to improve results and eliminate manual labor. By combining a multilayered neural network with cosine proximity com- parison, this thesis shows promising results in that a fully automated machine learning model can be implemented and replace the systems currently in use.}}, author = {{Åström, Joacim and Ekdahl, Alexander}}, issn = {{1650-2884}}, language = {{eng}}, note = {{Student Paper}}, series = {{LU-CS-EX 2017-29}}, title = {{Subjective matching of products using Machine Learning}}, year = {{2017}}, }