Dimensionality reduction in computer-aided decision making

Abstract Two methods for reducing dimensions in order to address the classification problem are shown here. Both methods are filters using information gain ratio to select feature subset from the original data. These methods are applied to fault detection for the Tennessee Eastman problem. In the case study, both decision tree inducer and support vector machine are used as the base learner. Classification results for test data are analyzed in detail.