Embedded feature selection for support vector machines: State-of-the-art and future challenges

Abstract

Recently, databases have incremented their size in all areas of knowledge, considering both the number of instances and attributes. Current data sets may handle hundreds of thousands of variables with a high level of redundancy and/or irrelevancy. This amount of data may cause several problems to many data mining algorithms in terms of performance and scalability. In this work we present the state-of-the-art the for embedded feature selection using the classification method Support Vector Machine (SVM), presenting two additional works that can handle the new challenges in this area, such as simultaneous feature and model selection and highly imbalanced binary classification. We compare our approaches with other state-of-the-art algorithms to demonstrate their effectiveness and efficiency. © 2011 Springer-Verlag.