Abstract:

The purpose of feature selection in machine learning is at least two-fold - saving measurement acquisition costs and reducing the negative effects of the curse of dimensionality with the aim to improve the accuracy of the models and the classification rate of classifiers with respect to previously unknown data. Yet it has been shown recently that the process of feature selection itself can be negatively affected by the very same curse of dimensionality - feature selection methods may easily over-fit or perform unstably. Such an outcome is unlikely to generalize well and the resulting recognition system may fail to deliver the expectable performance. In many tasks, it is therefore crucial to employ additional mechanisms of making the feature selection process more stable and resistant the curse of dimensionality effects. In this paper we discuss three different approaches to reducing this problem. We present an algorithmic extension applicable to various feature selection methods, capable of reducing excessive feature subset dependency not only on specific training data, but also on specific criterion function properties. Further, we discuss the concept of criteria ensembles, where various criteria vote about feature inclusion/removal and go on to provide a general definition of feature selection hybridization aimed at combining the advantages of dependent and independent criteria. The presented ideas are illustrated through examples and summarizing recommendations are given.