Feature selection via the discovery of simple classification rules
Geoffrey Holmes, Craig G. Nevill-Manning · 1995
It has been our experience that in order to obtain useful results using supervised learning of real-world datasets it is necessary to perform feature subset selection and to perform many experiments using computed aggregates from the most relevant features. It is, therefore, important to look for selection algorithms that work quickly and accurately so that these experiments can be performed in a reasonable length of time, preferably interactively. This paper suggests a method to achieve this using a very simple algorithm that gives good performance across different supervised learning schemes and when compared to one of the most common methods for feature subset selection. KEYWORDS Feature subset selection; supervised learning; 1R; filter model; wrapper model. INTRODUCTION There is growing evidence that feature subset selection can substantially improve the task of performing supervised learning. The algorithms that perform feature subset selection have been studied in a variety o...