Optimized Modularity-Based High Level Classification Model

Author(s)

Colliri, Tiago ; Liu, Weiguang ; Zhao, Liang ; IEEE

• Document type: Journal article
• Source: 2020 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2020-01-01.

Abstract

In this paper, we introduce a network-based classification model which, instead of mapping each data instance as a node in a network, as usual, it maps each data instance attribute as being a node. This procedure allows the model to preserve more information from the input dataset when building the network, specially for datasets with a larger number of features, and thus to make use of this extra information during the training phase. In addition, we also introduce a technique intended to generate a network with one component per class in the dataset while keeping the threshold parameter, which is responsible for determining the edges among the nodes, at a minimum value. In this way, the network emerging from this process is more sensitive to the insertion of new instances, during the testing phase, in terms of its modularity measure, which allows the classifier to infer the new labels based mainly on this measure. We evaluate the model by applying it to both artificial and real benchmark classification datasets, and have its performance compared to those obtained by other traditional classification models on the same data. The preliminary results are encouraging, with the proposed model being ranked on second place among the 10 classifiers considered, on the selected datasets. (AU)

• FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
• Grantee: Francisco Louzada Neto
• Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
• FAPESP's process: 15/50122-0 - Dynamic phenomena in complex networks: basics and applications
• Grantee: Elbert Einstein Nehrer Macau
• Support Opportunities: Research Projects - Thematic Grants