Credit Risk Prediction Using Fuzzy Immune Learning
Algorithm 1
An overview of the proposed classifier. At initialization, a population of B-cells is generated from instances of class . then some B-cells are selected to proliferate in rule generation phase. The life cycle of B-cells is controlled by age. The best B-cell is added to rule set if the classification rate increases more than a threshold. At last, if the termination test satisfies the classifier learns rules for class .
(1) procedure Proposed Classifier
(2) do
(3) Set current learning class as ;
(4) While Termination Test
(5) Generate initial B-cell repertoire from class c antigens;
(6) While cycle < Max_Iterations {
(7) Perform Clonal Selection Procedure;
(8) // Use three selection procedures as:
(9) //(1) Roulette Wheel Selection,
(10) //(2) Tournament Selection,
(11) //(3) Uniform Selection.
(12) Use memory (Simple and k-layered to clone selected B-Cells;
(13) Perform Hyper-mutation;
(14) }
(15) Perform Rule Learning Procedure;
(16) //(1) Select the best B cell
(17) //(2) Add rule of the best B cell to the current rule set
(18) If classification rate is not increased, then the current loop exits.
