Research Domain Selection using Naive Bayes Classification
Автор: Selvani Deepthi Kavila, Radhika Y
Журнал: International Journal of Mathematical Sciences and Computing(IJMSC) @ijmsc
Статья в выпуске: 2 vol.2, 2016 года.
Бесплатный доступ
Research Domain Selection plays an important role for researchers to identify a particular document based on their discipline or research areas. This paper presents a framework which consists of two phases. In the first phase, a word list is constructed for each area of the research paper. In the second phase, the word list is continuously updated based on the new domains of research documents. Primary area and Sub area of the documents are identified by applying pre-processing and text classification techniques. Naive Bayes classifier is used to find the probability of various areas. An area having the highest probability is considered as primary area of the document. In this paper text classification procedures is condensed as that are utilized to arrange the content archives into predefined classes. Based on the performance analysis, it has been observed that the obtained results are efficient when compared to manual judgement.
Research Domain Selection, Information Retrieval, Text mining, Classification
Короткий адрес: https://sciup.org/15010204
IDR: 15010204
Список литературы Research Domain Selection using Naive Bayes Classification
- Jian Ma, Wei Xu, Yong-hong Sun, Efraim Turban, Shouyang Wang, Ou Liu .An Ontology-Based Text-Mining Method to Cluster Proposals for Research Project Selection. IEEE transactions on systems, man and cybernetics part a: systems and humans, vol. 42, no 3, may 2012.
- Richa Sapra and Preet Kaur. Ontology Based Classification And Clustering Of Research Proposals and External Research Reviewers. International Journal of Computers & Technology, Volume 5, No. 1, May -June, 2013.
- N.Arunachalam, S.Hismath Begum, E.Sathya and M.Uma Makeswari. An Ontology Based Text Mining Framework for R&D Project Selection. International Journal of Computers and Technology, volume 5, No.1, February 2013.
- N. Gorla and K. Chen. Information system project selection using fuzzy logic. IEEE Transactions and Systems, Man and Cybernetics society, Systems and Humans, vol. 28, no. 6, pp. 849–855, Nov. 1998.
- A. D. Henriksen and A. J. Traynor. A practical R&D project-selection scoring tool. IEEE Transactions Engineering Management. vol. 46, no. 2, pp. 158–170, May 1999.
- F. Ghasemzadeh and N. P. Archer. Project portfolio selection through decision support. Decision Support Systems, vol. 29, no. 1, pp. 73–88, Jul. 2000.
- L. L. Machacha and P. Bhattacharya. A fuzzy-logic-based approach to project selection. IEEE Transactions Engineering Management. vol. 47, no. 1, pp. 65–73, Feb. 2000.
- J. Butler, D. J. Morrice, and P. W. Mullarkey. A multiple attribute utility theory approach to ranking and selection. Management Sciences, vol. 47, no. 6, pp. 800–816, Jun. 2001.
- C. H. Loch and S. Kavadias. Dynamic portfolio selection of NPD programs using marginal returns. Management Sciences, vol. 48, no. 10, pp. 1227– 1241, Oct. 2002.
- Murad Habib, Raza Khan and Javaid L. Piracha. Analytic network process applied to Research & Development project selection.
- M. A. Greiner, J. W. Fowler, D. L. Shunk, W. M. Carlyle, and R. T. Mcnett.A hybrid approach using the analytic hierarchy process and integer programming to screen weapon systems projects. IEEE Transactions Engineering and Management. vol. 50, no. 2, pp. 192–203, May 2003.
- C. Choi and Y. Park.R&D proposal screening system based on text mining approach.Int. J. Technology Intelligence Plan., volume. 2, no. 1, pp. 61–72, 2006.
- Y. H. Sun, J. Ma, Z. P. Fan, and J. Wang. A hybrid knowledge and model approach for reviewer assignment. Expert Systems. Applications, volume 34, no. 2, pp. 817–824, Feb. 2008.
- S. Hettich and M. Pazzani.Mining for proposal reviewers: Lessons learned at the National Science Foundation. in Proceedings. 12th International Conference Knowledge Discovery. Data Mining, 2006, pp. 862–871.
- C. P. Wei and Y. H. Chang.Discovering event evolution patterns from document sequences.IEEE Transactions Systems, Man, Cybern. A, Systems, Humans, vol. 37, no. 2, pp. 273–283, Mar. 2007.
- T. H. Cheng and C. P. Wei.A clustering-based approach for integrating document-category hierarchies. IEEE Transactions Systems, Man, Cybern. A, Systems, Humans, vol. 38, no. 2, pp. 410–424, Mar. 2008.
