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Bridging from syntactic to statistical methods: Classification with automatically segmented features from sequences
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
2015 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 11, 3749-3756 p.Article in journal (Refereed) Published
Abstract [en]

To integrate the benefits of statistical methods into syntactic pattern recognition, a Bridging Approach is proposed: (i) acquisition of a grammar per recognition class; (ii) comparison of the obtained grammars in order to find substructures of interest represented as sequences of terminal and/or non-terminal symbols and filling the feature vector with their counts; (iii) hierarchical feature selection and hierarchical classification, deducing and accounting for the domain taxonomy. The bridging approach has the benefits of syntactic methods: preserves structural relations and gives insights into the problem. Yet, it does not imply distance calculations and, thus, saves a non-trivial task-dependent design step. Instead it relies on statistical classification from many features. Our experiments concern a difficult problem of chemical toxicity prediction. The code and the data set are open-source. (C) 2015 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
2015. Vol. 48, no 11, 3749-3756 p.
Keyword [en]
Syntactic pattern recognition, Grammatical inference, Feature segmentation, SMILES parser, Feature extraction
National Category
Computer Systems
URN: urn:nbn:se:bth-10555DOI: 10.1016/j.patcog.2015.05.001ISI: 000359028900037OAI: diva2:853816
Available from: 2015-09-15 Created: 2015-09-14 Last updated: 2015-12-11Bibliographically approved

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Sidorova, Yulia
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