Entropy Weight Method Implementation in MATLAB for Weight Calculation

This document presents MATLAB implementation of entropy weight method for calculating criterion weights. The entropy weight method is a widely used multi-criteria decision-making approach that determines the relative importance of each criterion in decision-making processes. By computing the information entropy of each criterion, we obtain corresponding weights for balanced decision-making. Below is a comprehensive MATLAB code example demonstrating the entropy weight calculation process:

% Input data matrix - represents evaluation criteria across different alternatives X = [1 2 3; 4 5 6; 7 8 9];

% Step 1: Calculate probability distribution for each column (criterion) % Normalizes the data to create probability values using element-wise division P = X ./ sum(X);

% Step 2: Compute information entropy for each criterion % Uses logarithmic operations to measure uncertainty/information content H = -sum(P .* log2(P));

% Step 3: Calculate weights based on entropy values % Weights are inversely proportional to entropy - lower entropy indicates higher importance W = (1 - H) / sum(1 - H);

% Display the resulting weight vector % Higher weight values signify more important criteria in decision-making disp(W);

This implementation allows efficient calculation of entropy-based weights for multi-criteria analysis. Users can modify the input matrix X according to their specific dataset requirements. The code handles the complete entropy weight calculation workflow, including data normalization, entropy computation, and weight derivation. The resulting weight vector provides quantitative measures of criterion importance for objective decision support.