Binary particle swarm optimization for feature Selection on uterine electrohysterogram signal

The Selection of pertinent features is a very important problem in pattern recognition. Therefore, we need reliable feature selection methods to reduce the number of features, through the elimination of irrelevant and noisy features. In our study we try to detect the pertinent features extracted from uterine electrohysterography that permit to classify at best labor and pregnancy contractions. The global aim of this work is to detect the preterm deliveries. In this paper we present a feature selection method based on binary particle swarm optimization. The performance of this method was tested by using three types of classifiers. The results show that this method gives common features whatever the type of classifiers.