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To meet the huge traffic growth, heterogeneous networks composed of wireless local area networks (WLAN) and 3G cellular networks are used to provide higher capacity and coverage. When the two networks are available, selecting the best network for downloading data with minimum device energy consumption and high quality of service (QoS) becomes a challenging issue especially that mobile devices have limited energy capacity. This paper proposes a learning based approach for performing network selection based on real-network implementations. The main contributions are first, presenting an approach for building training data as a basis for machine learning of network selection and then developing the classification model for network selection. The model considers the features that affect the selection decision known by the user: availability of the networks, signal strength reflecting the channel quality …