Algorithm 1. The optimization steps of parameters using PSO.

Step 1: Input the training and testing data, initialize the parameters $C$ and $\sigma$ of SVDD model, and set the searchable range of the parameters.

Step 2: Initialize the particle swarm, including the population size $W$, acceleration constants $c_1$ and $c_2$, inertia weight $\omega$, maximum number of iterations $I_t$, and the particle speed and position.

Step 3: Determine the individual extremum of the initial position and the optimal position of the particle swarm.

Step 4: Calculate the fitness value of the new position of each particle in the swarm.

Step 5: Compare the current optimal position of each particle with the optimal position of the particle swarm and update the optimal solution to the current optimal position of particle swarm.

Step 6: Update the speed and position of the particle.

Step 7: Determine whether the SVDD model with the current parameters can minimize the error rate or reach the maximum number of iterations. If one of them is satisfied, the optimal parameters $C$ and $\sigma$ are obtained. Otherwise, return to step 4 to recalculate the particle fitness value.