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For WiFi-based indoor localization, optimal selection of features leads to the increased perceptibility of the localization procedure. It is essential to capture the important sets of Access Points (APs) that best defines the floor map for the positioning process. To maintain sustainable localization, the selection of APs enables scaling the solution and reducing the maintenance cost. In the present work, our contribution is twofold- the power of Particle Swarm Optimization is utilized for the selection of important APs. Then, a feature-based ensemble model is designed for the selected subsets of APs to retain the generality of localization performance. The base learners capture the different ambiance in the training and testing process. Extensive experimentation was carried out using the collected dataset from multiple smartphone devices. The proposed feature selection and training pipeline has also been tested with two …