Improving Automatic Text Classification by Integrated Feature Analysis

SUMMARY Feature transformation in automatic text classification (ATC) can lead to better classification performance. Furthermore dimen-sionality reduction is important in ATC. Hence, feature transformation and dimensionality reduction are performed to obtain lower computational costs with improved classification performance. However, feature transformation and dimension reduction techniques have been conventionally considered in isolation. In such cases classification performance can be lower than when integrated. Therefore, we propose an integrated feature analysis approach which improves the classification performance at lower dimen-sionality. Moreover, we propose a multiple feature integration technique which also improves classification effectiveness. key words: text classification/categorization, feature transformation, dimension reduction, principal component analysis, canonical discriminant analysis, integrated feature analysis, multiple feature integration