TECHNOLOGY

    This aim of this study is to propose a new classification framework as Networked Pattern Recognition (NEPAR) for different classification problems. In most research studies, classification focuses on either individual observations, which do not consider the dynamic interactions, which ignores the functional roles of observations. When capturing interactions, they just give a general idea about networks. In this study, we propose a unified approach that combines pattern classification techniques and dynamic interactions for better classification approach. Therefore, the NEPAR and five different classification methods (SVM, NB, LR, DT, and kNN) are developed by adding information from the proposed networks (as seen in Figure 2-3). Figure 1. Combining network metrics and pattern recognition. As seen in Figure 1, information from observations is extracted by building the network, and feature properties for each observation are used to classify the output. For the results, we compare three approaches: (1) classic approach that uses traditional pattern recognition techniques; (2) the networked approach that uses pattern recognition techniques on the network topology; and (3) the unified approach that combines network topology and real data with pattern recognition methods (see Figure 1). Figure 2. Networks for the Pima Indian diabetes dataset and Australian credit card approval dataset. More specifically, a new weighted heterogeneous similarity function is also proposed to estimate relationships among interactive events. In the second phase of the framework, combining pattern-recognition techniques with network-based approaches. In this research, we propose a new hybrid detection framework in the proposed network topology.