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To measure the degree of information granulation, granularity has attracted many researchers extensive attention in the field of granular computing. One of the important and widely accepted patterns is parameterized granularity. Based on such parameterized granularity, when solving the problem of attribute reduction, it is often necessary to calculate the reducts related to each parameter until all of the reducts have been obtained. Obviously, this method will result in high time consumption. To fill such a gap, a multi-granularity attribute reduction approach based on continuous parameters is proposed. Firstly, a new constraint related to attribute reduction is constructed by using the interval of continuous parameters and the monotonicity of uncertainty measure in rough set. Secondly, a forward greedy searching algorithm is designed to derive the continuous parameters based reducts. Finally, 8 UCI data sets are selected for experimental comparisons and analyses. The results show that compared with single granularity based reducts in terms of multiple parameters, attribute reduction related to continuous parameters can greatly reduce the elapsed time of obtaining reduct without causing significant changes in the classification performance. This study provides a new solution for multi-granularity based modeling and feature selection from a continuous perspective.