In binaural hearing aids, several beamforming algorithms can be used. These beamformers aim to enhance the target speech signal and preserve the binaural cues of the target source (e.g. with constraints on the target). However, the binaural cues of the other directional sources as well the background noise are often lost after processing. This affects the global impression of the acoustic scene, and it limits the perceptual separation of the sources by the hearing aids users. To help the hearing aids users to localize all the sound sources, it is important to keep the binaural cues of all directional sources and the background noise. Therefore, this work is devoted to find the best trade-off between the noise/interferers reduction and the cues preservations not only for the directional interferers but also for the background noise based on selection and mixing processes. In this thesis, some classification decision algorithms, which are based on different criteria such as the power, the power difference, and the coherence, are proposed to complete the selection and mixing processes. Simulations are completed using recorded signals provided by a hearing aid manufacturer to validate the performance of the proposed algorithm under different realistic acoustic scenarios. After detailed testing using different complex acoustic scenarios and different beamforming configurations, the results indicate that some of the proposed classification decision algorithms show good promise, in particular the classification decision algorithm based on coherence.