Activity recognition is a complex task of the Human Computer Interaction (HCI) domain with ever-increasing research interest. Human activity recognition has been specially addressed by the advances in pattern recognition. k-Nearest Neighbors(kNN) is a non-parametric classifier from pattern recognition theory, that mimics human decision making by taking previous experiences into consideration for segregating unknown objects. A novel fuzzy-rough model, based on granular computing for improvisation of the kNN classifier is proposed herewith. In this model, feature-wise fuzzy memberships are generated to fuzzify the feature space of the nearest neighbors of the test object. These neighbors fuzzified feature space are then aggregated into granules, based on their class-belongingness. From these, lower and upper approximation granules are generated using rough set theory to classify the test object. It is shown experimentally that this model outperforms the traditional kNN by 16.43% and Fuzzy-kNN by 10.25%, in the human activity recognition domain. Another novelty is in the efficient use of the fuzzy similarity relations in class-dependent granulated feature space, and, fuzzy-rough lower/upper approximations in the hybridization of the kNN classifier.
Supervised Feature Space Reduction for Multi-Label Nearest Neighbors
