This repository focuses on implementing X-Subspace Support Vector Data Description, specifically addressing the Trustworthiness of X (formerly Twitter) Users through a One-Class Classification Approach. We present an innovative regularization term designed for Subspace Support Vector Data Description (SSVDD) to enhance its performance for X user classification.
The codes are provided as .m (matlab) files to be executed in matlab. The codes are provided without any warranty or guarantee.
S-SVDD requires LIBSVM for SVDD. Before executing the codes, make sure that correct version (3.22) of LIBSVM for SVDD is installed already. In order to install LIBSVM for SVDD Please download zip file from HERE, put sources into libsvm-3.22 available HERE, and make the code. For more details about how to install libsvm, please refer HERE
In our experiments for "Trustworthiness of X Users: A One-Class Classification Approach" We choose the hyperparameter values from the following ranges.
• 𝛽 ∈ {10−2, 10−1, 100, 101, 102}, Controlling the importance of the regularization term
• 𝐶 ∈ {0.1, 0.2, 0.3, 0.4, 0.5}, Value of hyperparameter C
• 𝜎 ∈ {10−1, 100, 101, 102, 103}, Hyperparameter for the kernel, used in non-linear data description
• 𝑑 ∈ {1, 2, 3, 4, 5, 10, 20}, dimensionality of data in lower dimension
• 𝜂 ∈ {10−1, 100, 101, 102, 103}, Used as step size for gradient
• 𝑘 ∈ {1, 2, 3, . . . , 10}, Number of K-neighbors(kNN) (Hyperparameter for the Laplacian, Input as 'l')
• C ∈ {1, 2, 3, . . . , 10}. Number of clusters (L_w,L_b) (Hyperparameter for the Laplacian, input as 'l')
We fix the number of iterations to 10 for all variants of SSVDD.