4 research outputs found
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 \textit{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 patterns 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 \textit{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