ReliefF and SVM Example

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.

Input Data:
(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'

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% Code written by Frederik Doerr, Feb 2020 (MATLAB R2019b)
% Application: For 'Support Vector Machine - Introduction and Application'

% % % Reference (open access):
% 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
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