Using Meta Heuristic in Constructing Classification Process

• Main Authors: Wei Zhan Hung, 洪偉展
• Other Authors: Ping-Feng Pai
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/30996010198980394044

博士 === 國立暨南國際大學 === 國際企業學系 === 102 === Using knowledge and experiments to predict the trend of future events is the prerequisites of management. Classifiers are the main models used to predict future events. Data processed by preprocessors are employed to train classifiers and generate information for predicting future events. In this study, the data preprocessor includes four steps (1) data imputation (2) outlier detection (3) data distretization (4) feature selection. Different classifiers are suitable for different data preprocessors; and this procedure can be treated as a classification decision process. The classification decision process influences the classification accuracy. In previous literature, experts usually used trial and error method to determine the classification decision process. However, the trial and error process is time-consuming and can not guarantee to obtain the best classification decision process. This study uses meta-huristics to yield the near optimal classification decision process. Some data in UCI library were used to demonstrate the performance of proposed method. Finally, the experimental results, limitations of proposed method and future research directions were presented.