A binary classification procedure based on the correlation with response variable and principal components

• Main Authors: Chuang-Hui Chun, 莊惠鈞
• Other Authors: Wei-Ning Yang
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/773cha

碩士 === 國立臺灣科技大學 === 資訊管理系 === 106 === Binary classification method predicts the class of an object based on the associated feature vector. The main logic behind the classification is to regress the feature vector on the class variable. Due to the high correlation between feature variables, we apply principal component analysis to obtain the uncorrelated transformed features to mitigate the multicollinearity problem.For maintaining the simplicity and flexibility of modeling the relationship between the feature vector and the class variable, we propose to predict the class of an object based on an integrated feature which is the linear combination of the features and the powers of each individual feature.The weights on the original features and their powers, and the corresponding classification threshold are determined by the genetic algorithm to maximize the classification accuracy based on the training dataset.To speed up the convergence rate of the genetic algorithm, the solution obtained from the linear discrimination analysis is used as the starting point of the genetic algorithm.We apply the proposed procedure on the benchmark datasets, and experimental results demonstrate the effectiveness of our algorithm.