Improve Abstract Data with Feature Selection for Classification Techniques

Abstract:

A universal problem with text classification has a problem due to the high dimensionality of feature space, e.g. word frequency vectors. To overcome this problem, this paper proposed a feature selection which focuses on statistical pattern based on SVM Attribute. Experiments have shown that the determination of word importance may increase the speed of the classification algorithm and save their resource used significantly. The proposed method was studied by comparing classification performance among Decision Tree, Naïve Bayes, and Support Vector Machine. The results showed that Support Vector Machine was found to be the best algorithm with F-measure 93.6%. It is found that the feature selection can reduce dimensionality of data significantly.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 3699-3703
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.3699
• Online since: November 2011
• Authors: Vatinee Nuipian, Phayung Meesad, Pudsadee Boonrawd
• Keywords: Digital Library, Feature Selection, Support Vector Machine (SVM), SVM Attribute, Text Classification

References

[1] T. Joachims: Text Categorization with Support Vector Machines: Learning with many Relevant Features, on www. citeseerx. ist. psu. edu.

[2] B. Jiang, X. Ding, Lin-Tao Ma, Ying He, Tao Wang, and Wei-Wei Xie : A Hybrid Feature Selection Algorithm: Combination of Symmetrical Uncertainty and Genetic Algorithms, The Second International Symposium on Optimization and Systems Biology (OSB'08) Lijiang, China, October 31– November 3 (2008).

[3] P. Soucy, G. W. Mineau: Beyond TFIDF Weighting for Text Categorization in the Vector Space Model, on http: /www. ijcai. org/papers/0304. pdf.

[4] R. Baeza-Yate, B. Ribciro-Neto: Modern Information Retrieval, ACM Press, Addison Wesley, (1999).

[5] R. Kohavi and G. H. John: Wrappers for feature subset selection, Artificial Intelligence 97 (1997) , pp.273-324, ELSEVIER.

DOI: 10.1016/s0004-3702(97)00043-x

[6] P. Meesad, V. NuiPian, and P. Boonrawd: A Chi-Square-Test for Word Importance Differentiation in Text Classification, Proceedings of Computer Science and Information Technology Vol. 6 (2011), pp.110-114.

[7] S. Senthamarai Kannan and N. Ramaraj: A novel hybrid feature selection via Symmetrical Uncertainty ranking based local memetic search algorithm, Knowledge-Based Systems 23 (2010) 580–585 on www. elsevier. com/locate/knosys.

DOI: 10.1016/j.knosys.2010.03.016

[8] S. Zhang, Z. Zhao: Feature Selection Filtering Methods for Emotion Recognition in Chinese Speech Signal, 2008, IEEE on http: /ieeexplore. ieee. org/stamp/stamp. jsp?arnumber=04697464.

DOI: 10.1109/icosp.2008.4697464

[9] A. Ahmad, L. Dey: A feature selection technique for classificatory analysis, Pattern Recognition Letter 26 (2005), pp.43-56.

DOI: 10.1016/j.patrec.2004.08.015

[10] G. Salton, A. Wong, and C. S. Yang: A vector space model for automatic indexing, Journal of the American Society for Information Science, 18(11): 613-620, Nov. (1975).

DOI: 10.1145/361219.361220

[11] J. R, Quinlan: Induction of Decision Trees, Machine Learning 1(1), 2006, 81-106.

[12] D. Lewis: Naive Bayes at forty: The independence assumption in information retrieval, Proc. of European Conf. on Machine Learning, p.4–15, (1998).

DOI: 10.1007/bfb0026666

[13] V. Vapnik: The Nature of Statistical Learning Theory, Springer, New York, (1995).

[14] The ACM Portal is published by the Association for Computing Machinery. Copyright 2009-2010 , Inc. On http: /portal. acm. org/portal. cfm.

[15] LexTo : Thai Lexeme Tokenizer on http: /www. sansarn. com/lexto.