A study of mutual information based feature selection for case based reasoning in software cost estimation

Software cost estimation is one of the most crucial activities in software development process. In the past decades, many methods have been proposed for cost estimation. Case based reasoning (CBR) is one of these techniques. Feature selection is an important preprocessing stage of case based reasoning. Most existing feature selection methods of case based reasoning are 'wrappers' which can usually yield high fitting accuracy at the cost of high computational complexity and low explanation of the selected features. In our study, the mutual information based feature selection (MICBR) is proposed. This approach hybrids both 'wrapper' and 'filter' mechanism which is another kind of feature selector with much lower complexity than wrappers, and the features selected by filters are likely to be generalized to other conditions. The MICBR is then compared with popular feature selectors and the published works. The results show that the MICBR is an effective feature selector for case based reasoning by overcoming some of the limitations and computational complexities of other feature selection techniques in the field.