Simultaneous feature selection and classification based on genetic algorithms: an application to colonic polyp detection

Selecting a set of relevant features is a crucial step in the process of building robust classifiers. Searching all possible subsets of features is computationally impractical for large number of features. Generally, classifiers are used for the evaluation of the separability of a certain feature subset. The performance of these classifiers depends on some predefined parameters. However, the choice of these parameters for a given classifier is influenced by the given feature subset and vice versa. The computational cost for feature selection would be largely increased by including the selection of optimal parameters for the classifier (for each subset). This paper attempts to tackle the problem by introducing genetic algorithms (GAs) to combine the processes. The proposed approach can choose the most relevant features from a feature set whilst simultaneously optimising the parameters of the classifier. Its performance was tested on a colon polyp database from a cohort study using a weighted support vector machine (SVM) classifier. As a general approach, other classifiers such as artificial neural networks (ANN) and decision trees could be used. This approach could also be applied to other classification problems such as other computer aided detection/diagnosis applications.