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Journal of Sensors
Recent progress in the development of sensor devices improves information harvesting and allows complex but intelligent applications based on learning hidden relations between collected sensor data and objectives. In this scenario, multilabel feature selection can play an important role in achieving better learning accuracy when constrained with limited resources. However, existing multilabel feature selection methods are search-ineffective because generated feature subsets frequently includedoi:10.1155/2018/3419213 fatcat:36g6sg5qs5ehti23ap5hjhvznm