Browse > Article
http://dx.doi.org/10.3745/JIPS.2008.4.1.017

Inverted Index based Modified Version of KNN for Text Categorization  

Jo, Tae-Ho (School of Computer and Information Engineering Inha University)
Publication Information
Journal of Information Processing Systems / v.4, no.1, 2008 , pp. 17-26 More about this Journal
Abstract
This research proposes a new strategy where documents are encoded into string vectors and modified version of KNN to be adaptable to string vectors for text categorization. Traditionally, when KNN are used for pattern classification, raw data should be encoded into numerical vectors. This encoding may be difficult, depending on a given application area of pattern classification. For example, in text categorization, encoding full texts given as raw data into numerical vectors leads to two main problems: huge dimensionality and sparse distribution. In this research, we encode full texts into string vectors, and modify the supervised learning algorithms adaptable to string vectors for text categorization.
Keywords
String Vector; K- Nearest Neighbor; Text Categorization;
Citations & Related Records
연도 인용수 순위
  • Reference
1 N. Cristianini and J. Shawe-Taylor, Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000
2 M. Hearst, “Support Vector Machines”, IEEE Intelligent Systems, Vol 13, No 4, pp18-28, 1998   DOI   ScienceOn
3 D. Mladenic and M. Grobelink, “Feature Selection for unbalanced class distribution and Naive Bayes”, The Proceedings of International Conference on Machine Learning, pp256-267, 1999
4 E. D. Wiener, “A Neural Network Approach to Topic Spotting in Text”, The Thesis of Master of University of Colorado, 1995
5 T. Joachims, “Text Categorization with Support Vector Machines: Learning with many Relevant Features”, The Proceedings of 10th European Conference on Machine Learning, pp143-151, 1998
6 T. Jo, and N. Japkowicz, “Class Imbalances versus Small Disjuncts”, ACM SIGKDD Exploration Newsletters, Vol 6, No1, pp40-49, 2004   DOI
7 T. Jo and N. Japkowicz, “Text Clustering using NTSO”, The Proceedings of IJCNN, pp558-563, 2005
8 R. R. Korfahage, Information Storage and Retrieval, Wiley Computer Publishing, 1997
9 H. Lodhi, C. Saunders, J. Shawe-Taylor, N. Cristianini, and C. Watkins, “Text Classification with String Kernels, Journal of Machine Learning Research”, Vol 2, No 2, pp419-444, 2002   DOI
10 B. Massand, G. Linoff, and D. Waltz, “Classifying News Stories using Memory based Reasoning”, The Proceedings of 15th ACM International Conference on Research and Development in Information Retrieval, pp59-65, 1992
11 T. Mitchell, Machine Learning, McGraw-Hill, 1997
12 M. E. Ruiz and P. Srinivasan, “Hierarchical Text Categorization Using Neural Networks”, Information Retrieval, Vol 5, No 1, pp87-118, 2002   DOI   ScienceOn
13 F. Sebastiani, “Machine Learning in Automated Text Categorization”, ACM Computing Survey, Vol 34, No 1, pp1-47, 2002   DOI   ScienceOn
14 P. Jackson, and I. Mouliner, Natural Language Processing for Online Applications: Text Retrieval, Extraction and Categorization, John Benjamins Publishing Company, 2002
15 Y. Yang, “An evaluation of statistical approaches to text categorization”, Information Retrieval, Vol 1, No 1-2, pp67-88, 1999
16 Androutsopoulos, K. Koutsias, K. V. Chandrinos, and C. D. Spyropoulos, “An Experimental Comparison of Naive Bayes and Keyword-based Anti-spam Filtering with personal email message”, The Proceedings of 23rd ACM SIGIR, pp160-167, 2000
17 H. Drucker, D. Wu, and V. N. Vapnik, “Support Vector Machines for Spam Categorization”, IEEE Transaction on Neural Networks, Vol 10, No 5, pp1048-1054, 1999   DOI   ScienceOn
18 A. Estabrooks, T. Jo, and N . Japkowicz, “A Multiple Resampling Method for Learning from Imbalanced Data Sets”, Computational Intelligence, Vol 28, No 1, pp18-26, 2004   DOI   ScienceOn