Using A Hybrid Meta-evolutionary Algorithm for Mining Classification Rules Through Microarray Data

• Main Authors: Ting-Yi Yu, 尤婷藝
• Other Authors: 陳大正
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/5ns46j

碩士 === 國立虎尾科技大學 === 資訊管理研究所 === 98 === With the rapid development of information technology, microarray data is an important field of study for cancer research. However, microarray data is with high dimensional attributes and small sample size resulting in lengthy computation time and low classification accuracy. Due to gene microarray data classification issues, how to get more accurate prediction results with better quality becomes an important area of research. This thesis has proposed a hybrid evolutionary algorithm which combines a genetic algorithm and binary particle swarm optimization with fuzzy discriminate function. The proposed method is used to estimate the fitness value for classification, significant variables extraction, and parameters of fuzzy membership function in the meanwhile. Through the adjustment of the dimension of microarray data and the choice of membership function, fewer significantly characteristic attributes can reach high classification accuracy. Fuzzy rules can also be observed through data attributes and the relationship between categories. To reduce the vast computation time for classification process, this study integrates grid computing technology in the proposed approach. The experimental results show our proposed method can achieve higher classification accuracy and effectively reduces the computation time.