Hybrid method to supervise feature selection using signal processing and complex algebra techniques

Abstract

Research in AI has proved to be revolutionarily beneficial to humankind from the past few decades. Many supporting techniques have been developed that indirectly evolved AI and directly enhanced various machine learning models; one of them being the feature engineering. It can also be considered as applied-ML. Another is the so-called feature selection which is method in which most contributing feature to final decision making, out of the entire feature space are selected for processing into an ML model. It is not an easy task to precisely calculate the dependency of the output variable onto the candidate features, particularly when the data is high in dimensions. In this regard, this study proposes a novel method named the cisoidal analysis based feature selection (CAFS) which uses both pre-established algorithms as well as a new approach of relating members of feature space to a complex sinusoid (cisoid) mathematically, then using signal processing techniques to eliminate certain elements in the entire feature space for enhanced feature selection and hence to obtain higher classification accuracy. Derived from experiments with five high dimensional datasets, CAFS displays significantly competitive performance than some of the pre-existing algorithms. CAFS is highly advantageous in reducing dimension of feature space in most of the applications.