| Summary: | 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.
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