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We consider an approximation scheme for multivariate information assumingthat synergistic information only appearing in higher order joint distributionsis suppressed, which may hold in large classes of systems. Our approximationscheme gives a practical way to evaluate information among random variables andis expected to be applied to feature selection in machine learning. Thetruncation order of our approximation scheme is given by the order of synergy.In the classification of information, we use the partial informationdecomposition of the original one. The resulting multivariate information isexpected to be reasonable if higher order synergy is suppressed in the system.In addition, it is calculable in relatively easy way if the truncation order isnot so large. We also perform numerical experiments to check the validity ofour approximation scheme.

An Approximation Scheme for Multivariate Information based on Partial Information Decomposition