A New Hybrid Algorithm for Bankruptcy Prediction Using Switching Particle Swarm Optimization and Support Vector Machines

Abstract

Bankruptcy prediction has been extensively investigated by data mining techniques since it is a critical issue in the accounting and finance field. In this paper, a new hybrid algorithm combining switching particle swarm optimization (SPSO) and support vector machine (SVM) is proposed to solve the bankruptcy prediction problem. In particular, a recently developed SPSO algorithm is exploited to search the optimal parameter values of radial basis function (RBF) kernel of the SVM. The new algorithm can largely improve the explanatory power and the stability of the SVM. The proposed algorithm is successfully applied in the bankruptcy prediction problem, where experiment data sets are originally from the UCI Machine Learning Repository. The simulation results show the superiority of proposed algorithm over the traditional SVM-based methods combined with genetic algorithm (GA) or the particle swarm optimization (PSO) algorithm alone.

Support Vector Machine Methods and Artificial Neural Networks Used for the Development of Bankruptcy Prediction Models and their Comparison

“…SVM, firstly developed by Vapnik in 1995 (Vapnik 1995), is a supervised learning model with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis (Burges 1998). According to Lu et al (2015), compared with other algorithms, SVM has many unique advantages when applied in solving small sample, nonlinear, and high-dimensional pattern recognition problem. The concept of a neural network has been developed in biology and psychology, but its use goes to other areas, such as business and economics (Vochozka 2017).…”

Interval Forecasting of Carbon Futures Prices Using a Novel Hybrid Approach with Exogenous Variables

“…All the velocity components are assigned the initial value of 0. The parameters of PSO are determined in line with the recommendations in [41][42][43][44]. The final parameters of PSO are shown in Table 3.…”

Image Classification and Recognition of Rice Diseases: A Hybrid DBN and Particle Swarm Optimization Algorithm

“…7 is introduced to realize a balance between the local search algorithm and the global one. Lu et al (2015) have employed the SPSO algorithm to solve the optimization problem with constraints by converting it into an optimization problem without constraints and have gained a better performance.…”

“…In the study, the BP layer of DBN is replaced by the SPSO-SVM classifier (For detail of switching PSO to optimize the parameters of SVM, refer to the study by Lu et al (2015) .…”