Pattern-Based Classification via a High Level Approach Using Tourist Walks in Networks

Abstract

Traditional data classification considers only physical features (e.g., geometrical or statistical features) of the input data. Here, it is referred to low level classification. In contrast, the human (animal) brain performs both low and high orders of learning and it has 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 here called high level classification. In this paper, we present an alternative technique which combines both low and high level data classification techniques. The low level term can be implemented by any classification technique, while the high level term is realized by means of the extraction of the underlying network's features (graph) constructed from the input data, which measures the compliance of the test instances with the pattern formation of the training data. Out of various high level perspectives that can be utilized to capture semantical meaning, we utilize the dynamical features that are generated from a tourist walker in a networked environment. Specifically, a weighted combination of transient and cycle lengths are employed for that end. Furthermore, we show computer simulations with synthetic and widely accepted real-world data sets from the machine learning literature. Interestingly, our study shows that the proposed technique is able to further improve the already optimized performance of traditional classification techniques.