Pattern-oriented associative rule-based patent classification

Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.eswa.2009.07.069

Title:	Pattern-oriented associative rule-based patent classification
Authors:	He, C. Loh, H.T.
Keywords:	Associated rule-based approach Patent classification Text categorization TRIZ TRIZ Principles
Issue Date:	15-Mar-2010
Citation:	He, C., Loh, H.T. (2010-03-15). Pattern-oriented associative rule-based patent classification. Expert Systems with Applications 37 (3) : 2395-2404. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2009.07.069
Abstract:	This paper proposes an innovative pattern-oriented associative rule-based approach to construct automatic TRIZ-based patent classification system. Derived from associative rule-based text categorization, the new approach does not only discover the semantic relationship among features in a document by their co-occurrence, but also captures the syntactic information by manually generalized patterns. We choose 7 classes which address 20 of the 40 TRIZ Principles and perform experiments upon the binary set for each class. Compared with three currently popular classification algorithms (SVM, C4.5 and NB), the new approach shows some improvement. More importantly, this new approach has its own advantages, which were discussed in this paper as well. Crown Copyright © 2009.
Source Title:	Expert Systems with Applications
URI:	http://scholarbank.nus.edu.sg/handle/10635/61057
ISSN:	09574174
DOI:	10.1016/j.eswa.2009.07.069
Appears in Collections:	Staff Publications

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