CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Double Selection Genetic Algorithm for Information Extraction

by H. Balaji, A. Govardhan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 110 - Number 8
Year of Publication: 2015
Authors: H. Balaji, A. Govardhan
10.5120/19336-0812

H. Balaji, A. Govardhan . Double Selection Genetic Algorithm for Information Extraction. International Journal of Computer Applications. 110, 8 ( January 2015), 12-14. DOI=10.5120/19336-0812

@article{ 10.5120/19336-0812,
author = { H. Balaji, A. Govardhan },
title = { Double Selection Genetic Algorithm for Information Extraction },
journal = { International Journal of Computer Applications },
issue_date = { January 2015 },
volume = { 110 },
number = { 8 },
month = { January },
year = { 2015 },
issn = { 0975-8887 },
pages = { 12-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume110/number8/19336-0812/ },
doi = { 10.5120/19336-0812 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:45:49.413028+05:30
%A H. Balaji
%A A. Govardhan
%T Double Selection Genetic Algorithm for Information Extraction
%J International Journal of Computer Applications
%@ 0975-8887
%V 110
%N 8
%P 12-14
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data extraction might be characterized as the undertaking of naturally concentrating occurrences of detailed classes or relations from text. This paper exhibits another preparing system focused around enhanced GA and greatest probability technique to get HIDDEN MARKOV MODEL with improved state count and its model parameters for web data extraction. This strategy defeats the deficiencies of the moderate merging rate of the HIDDEN MARKOV MODEL approach. From explores of different avenues regarding the 2100 networks removed from proposed corpus. This strategy has capacity to find ideal topology in all cases. Enhanced Genetic calculation may be utilized for web data extraction by forming a duplicate in the accompanying way as every state is connected with its group that it needs to concentrate, for example, writer or book title. Every state transmits terms from group particular dissemination. It can take in the group particular unigram conveyance and the state move probabilities from preparing information by Improved Genetic calculation mixture operations. With a specific end goal to mark another web with groups, it treats the terms from the web as perceptions and recoups the no doubt state grouping with the Viterbi calculation. In this adjusted Genetic calculation is utilized to concentrate data utilizing Hidden markov models.

References
  1. D. Freitag and A. Mccallum, Information extraction with HIDDEN MARKOV MODELs and shrinkage. Proceedings of the AAAI'99 Workshop on Machine Learning for Information Extraction, pp. 31-36, 1999.
  2. D. Freitag and A. Mccallum, Information extraction with HIDDEN MARKOV MODEL structures learned by stochastic optimization. Proceedings of the Eighteenth Conference on Artificial Intelligence, pp. 584-589, 2000.
  3. K. Seymore , A. Mccallum and R. Rosenfeld, Learning hidden markov model structure for information extraction. AAAI'99 Workshop on Machine Learning for Information Extraction, pp. 37-42, 1999.
  4. D. Freitag , A. Mccallum and F. Pereira, Maximum entropy markov models for information extraction and segmentation. Proceedings of ICML-2000, pp. 591-598, 2000.
  5. D. Bouchaffra and J. Tan, Structural hidden markov models using a relation of equivalence:
  6. R. J. Mooney and U. Y. Nahm, Text mining with information extraction. Multilingualism and Electronic language Management, Proceedings of the 4th International MIDP Colloquium, pp. 141-160, 2005.
  7. X. H. Phan, S. Horiguchi and T. B. Ho, Automated data extraction from the web with conditional models. Int. J. Business Intelligence and Data mining, 2:191-209, 2005.
  8. S. Kwong, C. W. Chan, K. F. Man and K. S. Tang, Optimization of HIDDEN MARKOV MODEL topology and its model parameters by genetic algorithms, Pattern Recognition,34:509-522, 2001.
  9. Q. Y. Hong and S. Kwong, A genetic classification method for speaker recognition. Engineering Applications of Artificial intelligence, 18:13-19, 2005.
  10. A. Asllani and A. Lari, Using genetic algorithm for dynamic and multiple criteria web-site optimizations. European Journal of Operational Research, 176:1767-1777, 2007.
  11. M. Caramia, G. Felici and A. Pezzoli, Improving search results with data mining in a thematic search engine. Computers & operations Research, 31:2387-2404, 2004.
  12. H. Zhon, Y. C. Feng and L. M. Han, The hybrid heuristic genetic algorithm for job shop scheduling. Computers & Industrial Engineering, 40:191-200, 2001.
  13. Jiyi Xiao Lamei Zou Chuanqi Li, "Optimization of Hidden Markov Model by a Genetic Algorithm for Web Information Extraction".
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm Fitness Function Mutation Information Extraction

Powered by PhDFocusTM