IJMTES – A FAST CLUSTERING-BASED FEATURE SUBSET SELECTION ALGORITHM FOR HIGH DIMENSIONAL DATA

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : A FAST CLUSTERING-BASED FEATURE SUBSET SELECTION ALGORITHM FOR HIGH DIMENSIONAL DATA

Author’s Name : Vigneshwari B | R Selvakumar
unnamed

Volume 04 Issue 11 2017

ISSN no:  2348-3121

Page no: 51-54

Abstract – Feature selection is the process of identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. In this paper proposed the technique for web information gathering is Key word based technique.

Keywords – Web, Efficiency, Web User, Key Word Technique

References

  1. H. Almuallim and T.G. Dietterich, “Algorithms for Identifying Relevant Features,” Proc. Ninth Canadian Conf. Artificial Intelligence, pp. 38-45, 1992.
  2. H. Almuallim and T.G. Dietterich, “Learning Boolean Concepts in the Presence of Many Irrelevant Features,” Artificial Intelligence, vol. 69, nos. 1/2, pp. 279-305, 1994.
  3. L.D. Baker and A.K. McCallum, “Distributional Clustering of Words for Text Classification, Proc. 21st Ann. Int’l ACM SIGIR Conf. Research and Development in information Retrieval, pp. 96-103, 1998.
  4. R. Battiti, “Using Mutual Information for Selecting Features in Supervised Neural Net Learning,” IEEE Trans. Neural Networks, vol. 5, no. 4, pp. 537-550, July 1994.
  5. Bell D.A. and Wang, H., A formalism for relevance and its application in feature subset selection, Machine Learning, 41(2), pp 175-195, 2000.
  6. A. Arauzo-Azofra, J.M. Benitez, and J.L. Castro, “A Feature Set Measure Based on Relief,” Proc. Fifth Int’l Conf. Recent Advances in Soft Computing, pp. 104-109, 2004.
  7. Biesiada J. and Duch W., Features election for high-dimensional datała Pearson redundancy based filter, Advances in Soft Computing, 45, pp 242C249,2008.
  8. Butterworth R., Piatetsky-Shapiro G. and Simovici D.A., On Feature Selection through Clustering, In Proceedings of the Fifth IEEE international Conference on Data Mining, pp 581-584, 2005.
  9. Chanda P., Cho Y., Zhang A. and Ramanathan M., Mining of Attribute
  10. Interactions Using Information Theoretic Metrics, In Proceedings of IEEE international Conference on Data Mining Workshops, pp 350-355, 2009.
Scroll Up