In this paper, we present fraudulent email detection model using advanced feature choice. We extracted various kinds of features and compared the performance of each category of features with the others in terms of the fraudulent email detection rate. The different types of features are incorporated step by step. The detection of fraudulent email has been considered as a machine learning problem and it is evaluated using various classification algorithms and on CCM [1] which is authors\' previous cluster based classification model. The experiments have been performed on diverse feature sets and the different classification methods. The comparison of the results is also presented and the evaluations show that for the fraudulent email detection tasks, the feature set is more important regardless of classification method.