Different features of a sampling datum have different quality as a result the influence of the environment and the equipment precision.For the feature selection of this kind of heterogeneous data

both the accuracy and the reliability of the classifier determined by a feature subset are required to simultaneously consider

which enhances the difficulty of selecting features.The problem of the feature selection of heterogeneous data is focused on in this paper

and a method of selecting features is presented based on multi-objective particle swarm optimization.In this method

the above problem is first converted to a multi-objective optimization problem by regarding the probability of selecting a feature as the decision variable.When particle swarm optimization (PSO) is employed to solve the converted problem

the global guider of particles is generated by Gaussian sampling so as to improve the performance of Pareto solutions in distribution.In addition

the particle to be disturbed is determined according to the speed of updating a particle in the archive to help the swarm jump out of local optima.The proposed method is applied to classify several benchmark data sets

and the experimental results demonstrate its effectiveness.