ReliefF and SVM Example

version 1.0.1 (3.22 MB) by Frederik D
Example of using ReliefF (Matlab: relieff) and SVM (Matlab: fitcsvm) for the classification of pharmaceutical pellets.


Updated 28 Dec 2020

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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):

(2) User defined feature categories:

(3) Results of a feature sensitivity analysis:

% 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.
% Data repository:
% Video Abstract:

Cite As

Doerr, Frederik J. S., and Alastair J. Florence. “A Micro-XRT Image Analysis and Machine Learning Methodology for the Characterisation of Multi-Particulate Capsule Formulations.” International Journal of Pharmaceutics: X, vol. 2, Elsevier BV, Dec. 2020, p. 100041, doi:10.1016/j.ijpx.2020.100041.

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MATLAB Release Compatibility
Created with R2019b
Compatible with any release
Platform Compatibility
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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.