Semi-Supervised Learning of K-Nearest Neighbors Using a Nearest-Neighbor Self-Contained Criterion in for Mobile-Aware Service

Abstract

We propose a new K-nearest neighbor (KNN) algorithm based on a nearest-neighbor self-contained criterion (NNscKNN) by utilizing the unlabeled data information. Our algorithm incorporates other discriminant information to train KNN classifier. This new KNN scheme is also applied in a community detection algorithm for mobile-aware service: First, as the edges of networks, the social relation between mobile nodes is quantified with social network theory; second, we would construct the mobile nodes optimal path tree and calculate the similarity index of adjacent nodes; finally, the community dispersion is defined to evaluate the clustering results and measure the quality of community structure. Promising experiments on benchmarks demonstrate the effectiveness of our approach for recognition and detection tasks.

Mobile Crowd Sensing for Internet of Things: A Credible Crowdsourcing Model in Mobile-Sense Service

“…Users within same community usually have similar social attributes and service preferences, and they are not strangers to each other, but with a certain trust relationship between themselves. In [37], the optimal path tree, similarity index and community dispersion index are defined and calculated to achieve the community detection, and an effective partitioning algorithm is proposed for weighted and directed network. Therefore, we can further establish a credible route from SR to SP, and realize the effective forwarding of crowdsourcing requests by utilizing the users in overlapped areas between two communities.…”