High Level Classification Totally Based on Complex Networks

Abstract

Differently from traditional machine learning techniques applied to data classification, high level classification considers not only the physical features of the data (distance, similarity or distribution), but also the pattern formation of the data. In this latter case, a set of complex network measures are employed because of their abilities to capture spatial, functional and topological relations. Although high level techniques offer powerful features, good classification performance is usually obtained by combining them with some low level algorithms, which, in turn, reduces the efficiency of the overall technique. A priori, the reason is that low level and high level techniques provide different visions of classification. In this way, one cannot simply substitute another. This paper presents a data classification technique in which low level and high level classifications are embedded in a unique scheme, i.e., the proposed technique does not need a separated low level technique. The novelty is the use of a simple and recently proposed complex network measure, named component efficiency. Thus, our algorithm computes the efficiency of information exchanging among vertices in each component and the resulting values are used to drive the classification of the new instances i.e., a new instance will be classified into one of the components (class), in which their local features are in conformity with the insertion of the new instance. The experiments performed with artificial and real-world data sets show our approach totally based on complex networks is promising and it provides better results than some traditional classification techniques.