High-accuracy document classification with a new algorithm
- Author(s): T. Temel 1
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View affiliations
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Affiliations:
1:
Department of Mechatronics Engineering, Faculty of Natural Sciences , Architecture and Engineering, Bursa Technical University , 16320 Bursa , Turkey
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Affiliations:
1:
Department of Mechatronics Engineering, Faculty of Natural Sciences , Architecture and Engineering, Bursa Technical University , 16320 Bursa , Turkey
- Source:
Volume 54, Issue 17,
23
August
2018,
p.
1028 – 1030
DOI: 10.1049/el.2018.0790 , Print ISSN 0013-5194, Online ISSN 1350-911X
© The Institution of Engineering and Technology
Received
06/03/2018,
Published
20/07/2018
A new algorithm based on learning vector quantisation classifier is presented based on a modified proximity-measure, which enforces a predetermined correct classification level in training while using sliding-mode approach for stable variation in weight updates towards convergence. The proposed algorithm and some well-known counterparts are implemented by using Python libraries and compared in a task of text classification for document categorisation. Results reveal that the new classifier is a successful contender to those algorithms in terms of testing and training performances.
Inspec keywords: learning (artificial intelligence); pattern classification; text analysis
Other keywords: modified proximity-measure; sliding-mode approach; document categorisation; stable variation; Python libraries; high-accuracy document classification; text classification; learning vector quantisation classifier; weight updates; predetermined correct classification level
Subjects: Document processing and analysis techniques; Knowledge engineering techniques
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