Top

Soft Computing

Published in:

01-04-2015 | Focus

Normalized table-matching algorithm as approach to text categorization

Author: Taeho Jo

Published in: Soft Computing | Issue 4/2015

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

This research is concerned with the improved version of table-based matching algorithm as the approach to text categorization tasks. It is intended to tackle the three problems in encoding texts into numerical vectors and the unstable performance by the fluctuations from text lengths in the previous version. In this research, we encode texts into tables rather than into numerical vectors, define the similarity measure between two tables which is always as a normalized value between zero and one, and apply it to the tasks of text categorization. As the benefits from this research, we expect better performance by solving the three problems resulting from encoding texts into numerical vectors, and more stable performance by improving the previous version. Therefore, we empirically validate the proposed approach through the four sets of experiments, with respect to both performance and stability.

previous article Overlapped latent Dirichlet allocation for efficient image segmentation

next article Design of sliding-mode control based on fuzzy disturbance observer for minimization of switching gain and chattering

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Cover TM, Hart PE (1967) Nearest neighbor pattern classification. IEEE Trans Inf Theory 13:21–27CrossRefMATH

Cristianini N, Shawe-Taylor J (2000) Support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge, UKCrossRef

Drucker H, Wu D, Vapnik VN (1999) Support vector machines for spam categorization. IEEE Trans Neural Netw 10(5):1048–1054CrossRef

Eyheramendy S, Lewis D, Madigan D (2003) On the Naive Bayes model for text categorization. In: The Proceedings of the 9th international workshop on artificial intelligence and statistics, pp 165–171

Hearst M (1998) Support vector machines. IEEE Intell Syst 13(4):18–28

Jo T (2000) NeuroTextCategorizer: a new model of neural network for text categorization. In: The Proceedings of ICONIP 2000, pp 280–285

Jo T (2004) Machine learning based approach to text categorization with resampling methods. In: The Proceedings of the 8th world multi-conference on systemics, cybernetics and informatics, pp 93–98

Jo T, Lee M (2007) Mistaken driven and unconditional learning of NTC. Lect Notes Comput Sci 4491:1205–1214

Jo T, Cho D (2008) Index based approach for text categorization. Int J Math Comput Simul 2(1):127–132

Jo T (2008) Table based matching algorithm for soft categorization of news articles in Reuter 21578. J Korea Multimed Soc 11(6):875– 882

Jo T (2008) Single pass algorithm for text clustering by encoding documents into tables. J Korea Multimed Soc 11(12):1749–1757

Joachims T (1998) Text categorization with support vector machines: learning with many relevant features. In: The Proceedings of 10th European conference on machine learning, pp 143–151

Jo T, Seo J (2001) ’Text categorization oriented connectionist model. In: The Proceedings of ICCPOL 2001, pp 65–68

Kononenko I (1989) ID3, sequential Bayes, naive Bayes and Bayesian neural networks. In: The Proceedings of 4th European working session on learning, Montpellier, pp 91–98

Lee K, Kageura K (2007) Virtual relevant documents in text categorization with support vector machines. Inf Process Manag 43(4):902– 913

Lodhi H, Saunders C, Shawe-Taylor J, Cristianini N, Watkins C (2002) Text classification with string kernels. J Mach Learn Res 2(2):419–444MATH

Massand B, Linoff G, Waltz D (1992) Classifying news stories using memory based reasoning. In: The Proceedings of 15th ACM international conference on research and development in information retrieval, pp 59–65

McClelland J, Rumelhart D (1986) Parallel distributed processing, vol 1 and 2. MIT Press, Cambridge, MA, USA

Mitchell TM (1997) Machine learning. McGraw-Hill, SingaporeMATH

Mladenic D, Grobelink M (1999) Feature selection for unbalanced class distribution and Naive Bayes. In: The Proceedings of international conference on machine learning, pp 256–267

Peters C, Koster CHA (2002) Uncertainty-based noise reduction and term selection in text categorization. Lect Note Comput Sci 2291:248–267CrossRef

Ruiz ME, Srinivasan P (2002) Hierarchical text categorization using neural networks. Inf Retr 5(1):87–118CrossRefMATH

Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47CrossRef

Snchez SN, Triantaphyllou E, Kraft D (2002) A feature mining based approach for the classification of text documents into disjoint classes. Inf Process Manag 38(4):583–604CrossRef

Tai X, Ren F, Kita K (2002) An information retrieval model based on vector space method by supervised learning. Inf Process Manag 38(6):749–764CrossRefMATH

Wang C, Wang W (2005) Using term clustering and supervised term affinity construction to boost text classification. Lect Note Comput Sci 3518:813–819CrossRef

Wiener ED (1995) A neural network approach to topic spotting in text. The Thesis of Master of University of Colorado

Yang Y (1999) An evaluation of statistical approaches to text categorization. Inf Retr 1(1–2):67–88

Title: Normalized table-matching algorithm as approach to text categorization
Author: Taeho Jo
Publication date: 01-04-2015
Publisher: Springer Berlin Heidelberg
Published in: Soft Computing / Issue 4/2015
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-014-1411-9

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Other articles of this Issue 4/2015

Ranking distributed database in tuple-level uncertainty

A genetic interval type-2 fuzzy logic-based approach for generating interpretable linguistic models for the brain P300 phenomena recorded via brain–computer interfaces

Dimensionality reduction of medical big data using neural-fuzzy classifier

Bacterially inspired evolution of intelligent systems under constantly changing environments

Random-key cuckoo search for the travelling salesman problem

Abductive inference in Bayesian networks using distributed overlapping swarm intelligence

Premium Partner