Strengthening learning algorithms by feature discovery

This paper presents a new feature discovery approach called FEADIS that strengthens learning algorithms with discovered features. The discovered features are formed by various mathematical functions including ceil, mod, sin, and similar. These features are constructed in an iterative manner to improve gradually its learning performance. We demonstrate FEADIS capabilities by testing different types of datasets including periodical datasets. From the results, we conclude that FEADIS increases the performance of learning algorithms in a wide range of datasets for nominal or numeric target feature. Furthermore, most of the well known classifiers without FEADIS strengthening have severe difficulty in handling datasets that have periodical functional relations between input features and target feature - a difficulty circumvented by their potential use of FEADIS.