WASDMC

Implementation of a 3-layer feed-forward neural network model for multiclass classification that was trained using a weights-and-structure-determination for multiclass classification (WASDMC) algorithm.
The purpose of this package is to classify occupations based on the International Standard Classification of Occupations (ISCO) through a WASDMC-based neural network.
The main articles used are the followings:

• D. Lagios, S.D. Mourtas, P. Zervas and G. Tzimas, "A Weights Direct Determination Neural Network for International Standard Classification of Occupations," Mathematics, 11(3), 629 (2023)

Also, the kit includes the following two datasets:

• ILO-ISCO, taken from https://www.ilo.org/ilostat-files/ISCO/newdocs-08-2021/ISCO-08/ISCO-08\%20EN\%20Structure\%20and\%20definitions.xlsx
• OS-ISCO, taken from https://doi.org/10.1007/978-3-031-08341-9_27

M-files Description

• Main_WASDMC.m: the main function
• problem.m: input data of the neural networks
• preprocessText.m: function for preprocessing data
• problem_figures.m: figures illustrating the problem's findings
• WASDMC.m: function for finding the optimal number of hidden-layer neurons, along with the optimal weights of the neural network
• WASDMC_PAF.m: function for finding the optimal number of hidden-layer neurons, along with the optimal weights of the neural network (includes alternative activation function)
• Zmatrix.m: function for calculating the matrix Z
• Zmatrix_PAF.m: function for calculating the matrix Z (includes alternative activation function)
• predictN.m: function for predicting
• predictN_PAF.m: function for predicting (includes alternative activation function)
• error_pred.m: function for calculating the mean absolute error (MAE) of the prediction
• FTree.mat: MATLAB's fine Tree model
• FKNN.mat: MATLAB's fine KNN model
• EBT.mat: MATLAB's Ensemble Bagged Trees model
• NNN.mat: MATLAB's Narrow Neural Network model

Also, the kit includes the following two functions:

• kappa.m, taken from https://www.mathworks.com/matlabcentral/fileexchange/15365-cohen-s-kappa
• confusion.m, taken from https://www.mathworks.com/matlabcentral/fileexchange/60900-multi-class-confusion-matrix?s_tid=srchtitle

Installation

• Unzip the file you just downloaded and copy the WASDMC directory to a location,e.g.,/my-directory/
• Run Matlab/ Octave, Go to /my-directory/WASDMC/ at the command prompt
• run 'Main_WASDMC (Matlab/Octave)

Results

After running the 'Main_WASDMC.m file, the package outputs are the following:

• The optimal number of hidden-layer neurons.
• The neural network models' statistics on the testing set of the datasets.
• The graphic illustration of the classification performance.

Environment

The WASDMC kit has been tested in Matlab 2021a on OS: Windows 10 64-bit.