Since wearable computing systems have grown in importance in the last years, there is an increased interest in implementing machine learning algorithms with reduced precision parameters/computations. Not only learning, also feature selection, most of the times a mandatory preprocessing step in machine learning, is often constrained by the available computational resources. This work considers mutual information – one of the most common measures of dependence used in feature selection algorithms – with a limited number of bits. In order to test the procedure designed, we have implemented it in several well-known feature selection algorithms. Experimental results over several synthetic and real datasets demonstrate that low bit representations are sufficient to achieve performances close to that of double precision parameters and thus open the door for the use of feature selection in embedded platforms that minimize the energy consumption and carbon emissions.