This repository was created for anybody interested in using feature selection (ReliefF, Matlab: relieff) and support vector machines (SVM, Matlab: fitcsvm) as a minimum working example to reproduce steps described in the publication below (Doerr2020). Data is provided in the sub-folder '_Data'. Structural features were extracted from micro-X-ray tomography data. ReliefF and SVM were used to build a classifier for the detection of broken pharmaceutical pellets within the sample.
Code written by Frederik Doerr (@frederik-d), Feb 2020 (MATLAB R2019b)
Application: For 'Support Vector Machine - Introduction and Application'
Contact: frederik.doerr(at)strath.ac.uk / CMAC (http://www.cmac.ac.uk/)
Doerr, F. J. S., Florence, A. J. (2020). A micro-XRT image analysis and machine learning methodology for the characterisation of multi-particulate capsule formulations. International Journal of Pharmaceutics: X. https://doi.org/10.1016/j.ijpx.2020.100041
Data repository: https://doi.org/10.15129/e5d22969-77d4-46a8-83b8-818b50d8ff45
Video Abstract: https://strathprints.strath.ac.uk/id/eprint/71463
Slide Deck: https://doi.org/10.13140/RG.2.2.26289.20322
(1) Extracted features of six ibuprofen (IBU) capsules (1763 pellets, 206 features):
- 'Desc_DataFile_C0.csv'
- 'Desc_DataFile_C1.csv'
- 'Desc_DataFile_C2.csv'
- 'Desc_DataFile_C3.csv'
- 'Desc_DataFile_C4.csv'
- 'Desc_DataFile_C5.csv'
(2) User defined feature categories:
- 'Feature_Categories.csv'
(3) Results of a feature sensitivity analysis:
- 'Feature_SenAnlys_Score.csv'