A HYBRID MODEL FOR BUSINESS FAILURE PREDICTION – UTILIZATION OF PARTICLE SWARM OPTIMIZATION AND SUPPORT VECTOR MACHINES

This paper proposes that the PSO- SVM approach could be a more suitable method for predicting potential flnancial distress and empirical results show that PSO integrated with SVM provides better classiflcation accuracy than the Grid search, and genetic algorithm with S VM approaches for companies as normal or under threat.

Abstract

Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment.As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks.Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions.In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection.For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used.As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms.A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).