1 Citation (Scopus)
29 Downloads (Pure)

Abstract

Structures of tree topology are frequently encountered in nature and in a range of scientific domains. In this paper, a multi-step framework is presented to classify tree topologies introducing the idea of elastic matching of their sequence encodings. Initially, representative sequences of the branching topologies are obtained using node labeling and tree traversal schemes. The similarity between tree topologies is then quantified by applying elastic matching techniques. The resulting sequence alignment reveals corresponding node groups providing a better understanding of matching tree topologies. The new similarity approach is explored using various classification algorithms and is applied to a medical dataset outperforming state-of-the-art techniques by at least 6.6% and 3.5% in terms of absolute specificity and accuracy correspondingly.
Original languageEnglish
Pages (from-to)151-159
Number of pages9
JournalNeurocomputing
Volume163
Early online date24 Feb 2015
DOIs
Publication statusPublished - 2 Sept 2015

Keywords

  • tree structures
  • elastic matching
  • breast ductal trees

Fingerprint

Dive into the research topics of 'Classifying tree structures using elastic matching of sequence encodings'. Together they form a unique fingerprint.

Cite this