Wrapper Feature Selection

INTRODUCTION It is well known that the performance of most data mining algorithms can be deteriorated by features that do not add any value to learning tasks. Feature selection can be used to limit the effects of such features by seeking only the relevant subset from the original features (de Souza et al., 2006). This subset of the relevant features is discovered by removing those that are considered as irrelevant or redundant. By reducing the number of features in this way, the time taken to perform classification is significantly reduced; the reduced dataset is easier to handle as fewer training instances are needed (because fewer features are present), subsequently resulting in simpler classifiers which are often more accurate. Due to the abovementioned benefits, feature selection has been widely applied to reduce the number of features in many data mining applications where data have hundreds or even thousands of features. A large number of approaches exist for performing feature selection including filters (Kira & Rendell, 1992), wrappers (Kohavi & John, 1997), and embedded methods (Quinlan, 1993). Among these approaches, the wrapper appears to be the most popularly used approach. Wrappers have proven popular in many research areas, including Bioinformatics (Ni & Liu, 2004), image classification (Puig & Garcia, 2006) and web page classification (Piramuthu, 2003). One of the reasons for the popularity of wrappers is that they make use of a classifier to help in the selection of the most relevant feature subset (John et al., 1994). On the other hand, the remaining methods, especially filters, evaluate the merit of a feature subset based on the characteristics of the data and statistical measures, e.g., chi-square, rather than the classifiers intended for use (Huang et al., 2007). Discarding the classifier when performing feature selection can subsequently result in poor classification performance. This is because the relevant feature subset will not reflect the classifier's specific characteristics. In this way, the resulting subset may not contain those features that are most relevant to the classifier and learning task. The wrapper is therefore superior to other feature selection methods like filters since it finds feature subsets that are more suited to the data mining problem. These differences between wrappers and other existing feature selection techniques have been reviewed by a number of studies (e.g. Huan & Lei, 2005). However, such studies have primarily focussed on providing a holistic view of all the different types of …