Investigation of Machine Learning Applicability on Mobile Device Diagnostics for Quality Prediction
(2016) In Master's Theses in Mathematical Sciences FMA820 20161Mathematics (Faculty of Engineering)
- Abstract
- Sony Mobile Communications (SoMC) perpetually collect large amounts of prototype device data into their database which is used for analysing their devices. Different areas are analysed individually using this data but no general model exists which encapsulates the overall performance. A desired model would include information from the different key performance areas and utilise machine learning with the objective to prognosticate the prototype device quality.
This thesis investigates the possibility of using machine learning tools to generalise the data analysis and build a predictive quality model based on historical data. It consists of extracting and processing data from the company database, finding a relevant feature representation... (More) - Sony Mobile Communications (SoMC) perpetually collect large amounts of prototype device data into their database which is used for analysing their devices. Different areas are analysed individually using this data but no general model exists which encapsulates the overall performance. A desired model would include information from the different key performance areas and utilise machine learning with the objective to prognosticate the prototype device quality.
This thesis investigates the possibility of using machine learning tools to generalise the data analysis and build a predictive quality model based on historical data. It consists of extracting and processing data from the company database, finding a relevant feature representation and evaluating different models based on both supervised and unsupervised learning methods. The study builds on the assumption that performance is related to the device software and its development, through which conclusions about quality could then be made.
The results show that finding relevant features that distinguish different device software is very difficult, rendering an unsupervised approach inadequate. Apart from deficient features, the supervised methods suffer from the unreliability of using survey data as labels. In conclusion, no explicit model is recommended and different approaches are preferable where another feature representation and more qualitative labels are the main requirements. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8881112
- author
- Dagasan, Edin LU and Josefsson, Rasmus LU
- supervisor
- organization
- course
- FMA820 20161
- year
- 2016
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- big data, machine learning, data mining, mobile device diagnostics
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3295-2016
- ISSN
- 1404-6342
- other publication id
- 2016:E22
- language
- English
- id
- 8881112
- date added to LUP
- 2016-08-25 13:40:37
- date last changed
- 2016-08-25 15:49:45
@misc{8881112, abstract = {{Sony Mobile Communications (SoMC) perpetually collect large amounts of prototype device data into their database which is used for analysing their devices. Different areas are analysed individually using this data but no general model exists which encapsulates the overall performance. A desired model would include information from the different key performance areas and utilise machine learning with the objective to prognosticate the prototype device quality. This thesis investigates the possibility of using machine learning tools to generalise the data analysis and build a predictive quality model based on historical data. It consists of extracting and processing data from the company database, finding a relevant feature representation and evaluating different models based on both supervised and unsupervised learning methods. The study builds on the assumption that performance is related to the device software and its development, through which conclusions about quality could then be made. The results show that finding relevant features that distinguish different device software is very difficult, rendering an unsupervised approach inadequate. Apart from deficient features, the supervised methods suffer from the unreliability of using survey data as labels. In conclusion, no explicit model is recommended and different approaches are preferable where another feature representation and more qualitative labels are the main requirements.}}, author = {{Dagasan, Edin and Josefsson, Rasmus}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Investigation of Machine Learning Applicability on Mobile Device Diagnostics for Quality Prediction}}, year = {{2016}}, }