A Network-Based High-Level Data Classification Algorithm Using Betweenness Centrality

Data classification is a major machine learning paradigm, which has been widely applied to solve a large number of real-world problems. Traditional data classification techniques consider only physical features (e.g., distance, similarity, or distribution) of the input data. For this reason, those are called low-level classification. On the other hand, the human (animal) brain performs both low and high orders of learning, and it has a facility in identifying pat-terns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is referred to as high-level classification. Several high-level classification techniques have been developed, which make use of complex networks to characterize data patterns and have obtained promising results. In this paper, we propose a pure network-based high-level classification technique that uses the betweenness centrality measure. We test this model in nine different real datasets and compare it with other nine traditional and well-known classification models. The results show us a competent classification performance.

DELAY CONTROL NETWORK CODING TECHNIQUE

Network coding is a technique known to efficiently utilize the bandwidth by exploiting the broadcast nature of the wireless medium. Network coding reduces the number of retransmissions by allowing the relay not only to forward the packets, but to do some logic operation. However, considering the randomness and the asymmetric nature of the traffic in the wireless medium, it is usually very challenging for the relay to predict when the next packet is coming, thus the main question for the relay when receives a packet is whether to hold the packet in order to obtain a network coding opportunity or to rebroadcast the packet directly and eliminate the delay. In this paper, we address this challenge by introducing two schemes; Bandwidth Consideration Scheme (BCS) which considers pure network coding to achieve the maximum improvement in network throughput, and Time Limited Scheme (TLS), which uses the network coding but considers the imposed delay. The results show that, BCS can lead to up to 50% improvement in the bandwidth, however for symmetric flows using pure network coding leads to unbounded delay. On the other hand, TLS noticeably decreases the imposed delay for the symmetric flows and leads to relatively similar improvement in the throughput for asymmetric flows.