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Activity recognition with mobile sensors is a challenging task due to the inherent noisy nature of the input data and resource limitations of the target platform. This paper presents a novel method of hybridizing classifier selection and classifier fusion in order to address these difficulties. It efficiently decreases the computational cost by activating appropriate classifiers according to the characteristics of the given input, and resolves the pattern variations by combining the chosendoi:10.1109/icsmc.2011.6083808 dblp:conf/smc/MinC11 fatcat:4o7utvni7rexfjrjjdnmsbqmrq