High-level pattern-based classification via tourist walks in networks

• Proposal of a novel hybrid (low- and high-level) classification technique.
• The high-level term is realized by a combination of several tourist walk processes.
• Tourist walks are used in a total novel approach: high-level classification.
• The high-level model’s learning weights are self-adjusted in a statistical way.
• Simulations on a real-world application: handwritten digit recognition.

In this paper, we present a hybrid classification technique, which combines the decisions of low- and high-level classifiers. The low-level term realizes the classification task considering only the input data’s physical features, such as geometrical or statistical characteristics. In contrast, the high-level classification process checks the compliance of the new test instances against the pattern formations of each class that composes the training data. For this end, we extract suitable organizational and topological descriptors of a network that is constructed from the input data. With these descriptors, we show that the high-level term has the ability of detecting data patterns with semantic and global meanings. Here, the input data’s pattern formations are extracted by utilizing the dynamical information generated from several tourist walk processes, which are performed on the resulting network. Specifically, weighted combinations of transient and cycle lengths, which are derived variables from the tourist walks, are employed. Moreover, we show an effective method for calibrating the learning weights of these terms by using a statistical approach. Furthermore, we show that the tourist’s memory size is related to what extent one may capture organizational and complex features of the network. This means that local, quasi-local, and global features can be extracted, depending on the value of memory size parameter. Still in this work, we uncover the existence of a critical memory length, here denominated complex saturation, where any values larger than this critical point make no changes in the behaviors of the transient and cycle lengths. We also investigate several artificial and real-world situations where the low-level term alone fails to identify intrinsic data patterns, but the high-level term is able to perform well. Our investigation suggests that the proposed technique is able to improve the already optimized performance of traditional classification techniques. Finally, we apply the proposed technique in recognizing handwritten digits images and interesting results are obtained.