Adaptive Granules-Based Semi-Supervised Feature Selection for Hybrid Data

The surge in online interactions and advancement in Big Data related techniques have generated vast amounts of hybrid data in the sense that the data are symbolic, numerical or missing features, and usually only a small number of data objects possess true labels due to high annotation costs. A necessary step of fully releasing the potential of these partially labeled hybrid data lies in feature selection, for which the neighborhood rough set (NRS) is an efficient mathematical method to apply. In NRS, setting proper neighborhood granules greatly influences the effectiveness and robustness of algorithms atop it. However, existing methods usually determine the optimal neighborhood radius of neighborhood granule via computationally intensive grid search, where the neighborhood radius for each object is the same, i.e., 'unadaptive'. Some methods investigate adaptive granulation strategies, yet they inevitably hinge on preset parameters or a-prior knowledge. To tackle this problem, we propose an adaptive granules-enabled semi-supervised feature selection method that can adaptively generate suitable neighborhood radii for both labeled and unlabeled objects. The core idea lies in using the purity of decision labels as the threshold for granularity maximization construction. Then, by combining with neighborhood entropy and local density, a feature metric is designed to measure the feature significance. A semi-supervised feature selection algorithm is utilized to select feature subset by using the information from both labeled and unlabeled objects. Instead of hinging on expert knowledge, the proposed method only rely on the data per se. Experimental results on real-world datasets demonstrate the effectiveness of the designed method and its superiority over other state-of-the-art.