A heuristic approach to efficient production of detector sets for an artificial immune algorithm-based bankruptcy prediction system

Bankruptcy prediction has been extensively studied. These studies provide a rich library of important variables to be considered in predicting whether a particular company faces bankruptcy. Furthermore, systems designers can utilize the findings of these studies as a reservoir of knowledge that complements the knowledge accumulated from the advancement of computer immunology in designing and developing a bankruptcy prediction system. In this paper, the author proposes a heuristic approach to efficient production of detector sets for an artificial immune algorithm (ARIA) that takes advantages of the knowledge derived from bankruptcy prediction literature, and explores the issues related to time and space complexities of different artificial immune algorithms. Furthermore, he provides a preliminary evidence on the time complexity associated with the new approach to detector set production and designing an ARIA-based bankruptcy prediction system