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Bridging from syntactic to statistical methods: Classification with automatically segmented features from sequences

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URI: https://hdl.handle.net/10016/33552
ISSN: 0031-3203
DOI: https://doi.org/10.1016/j.patcog.2015.05.001
UXXI: AR/0000017158
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bridging_PR_2015_ps.pdf (1.43 MB)
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2015-11-01
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Elsevier
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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.
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syntactic pattern recognition, grammatical inference, feature segmentation, smiles parser, feature extraction
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Sidorova, J., García, J. (2015). Bridging from syntactic to statistical methods: Classification with automatically segmented features from sequences. Pattern Recognition, 48, pp. 3749-3756. https://doi.org/10.1016/j.patcog.2015.05.001
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