Incremental Learning Based Artificial Immune System for Classification Problems

• Main Authors: Hsuan-Ming Chen, 陳炫明
• Other Authors: 張百棧
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/51405855935582195968

碩士 === 元智大學 === 資訊管理學系 === 100 === With the advance of information technology and the improvement of computing power, more and more problems has been solved and relied by the computer nowadays. Nevertheless the classification problems have been accounted for a sizeable proportion in the reality, therefore, it is worth a further immersing to this topic. In the past, classification problems that solved by the algorithm or model end up with a good result are often seen. However, the stability of the solution for many classification problems is not ideal enough. In recent years the design of the heuristic algorithm on many issues can be quite good results. It's mainly based on the way of evolution that learns the characteristics of the problem itself and expects to use iterative search to find a viable solution. This study hopes to construct a method based on a kind of evolutionary algorithms, artificial immune system, to develop an artificial immune classification algorithm and solve classification problems. The algorithm consists of clonal selection, antibody grading mechanism and new antibody expansion mechanism, among the use of incremental learning method to evolve new antibody and through these designs, expecting the improvement of overall efficiency and convergence of the algorithm. In this study, experimental design is mainly to solve intrusion detection and credit approval problems. The experimental results show that the proposed artificial immune system combined with incremental learning brings out positive results in different classification problems. The artificial immune classification algorithm in this study compared with other classification algorithms, the proposed method with a considerable degree of accuracy and stability.