A hybrid support vectors machine model with KSIR and HBMO in analyzing medical data

• Main Authors: Ying-Zhieh Cho, 卓盈玠
• Other Authors: Ping-Feng Pai
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/35606502724047971739

碩士 === 國立暨南國際大學 === 資訊管理學系 === 97 === Swarm intelligence is based on observing the collective behavior of social insects and extract characteristics that can be applied to human life domains, such as ant colony optimization (ACO), particle swarm optimization (PSO) and genetic algorithm (GA). This paper proposes a hybrid model which firstly combines factor analysis (FA) with kernel sliced inverse regression (KSIR) for attribute extraction and dimensionality reduction forming the best selected feature subset. Secondly, honey-bee mating optimization (HBMO) is used to solve the problem of parameters settings in support vector machine (SVM) for classification. Results of the medical dataset from the UCI Machine Learning Repository applying the hybrid model show better results than original methods. Thus, the proposed model is an alternative and helpful scheme in analyzing medical data.