| Title: |
Dynamic Feature Space and Incremental Feature Selection for the Classification of Textual Data Streams |
| Author(s): |
I. Katakis, G. Tsoumakas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (10 pages).
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| Keywords: |
Text Classification, Data Streams, Concept Drift, Dynamic Feature Space, Incremental Feature Selection.
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| Appeared in: |
ECML/PKDD-2006 International Workshop on Knowledge Discovery from Data Streams, pp. 107-116, Berlin, Germany, 2006.
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| Abstract: |
Real world text classification applications are of special interest for the machine learning and data mining community, mainly because they introduce and combine a number of special difficulties. They deal with high dimensional, streaming, unstructured, and, in many occasions, concept drifting data. Another important peculiarity of streaming text, not adequately discussed in the relative literature, is the fact that the feature space is initially unavailable. In this paper, we discuss this aspect of textual data streams. We underline the necessity for a dynamic feature space and the utility of incremental feature selection in streaming text classification tasks. In addition, we describe a computationally undemanding incremental learning framework that could serve as a baseline in the field. Finally, we introduce a new concept drifting dataset which could assist other researchers in the evaluation of new methodologies. |
| See also : |
Concept drifting datasets descibed in paper
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This paper has been cited by the following:
| 1 |
Kim, H.J., Chang, J.: Integrating incremental feature weighting into naïve bayes text classifier, Proc. 6th International Conference on Machine Learning and Cybernetics (ICMLC 2007), Volume 2, 2007, pp. 1137-1143.
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| 2 |
Kim, Han-Joon and Chang, Jae-Young, "Improving Naive Bayes Text Classifiers with Incremental Feature Weighting", KIPS Transactions, Korea Information Processing Society, b15 (5), pp. 457-464, 2008 |
| 3 |
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| 4 |
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| 6 |
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| 7 |
Han-Joon Kim, "Improving Techniques for Naive Bayes Text Classifiers", in Handbook of Research on Text and Web Mining Technologies, Min Song and Yi-Fang We (Eds), pp. 111-127, Idea Group, 2009. |
| 8 |
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| 9 |
Mohammad M. Masud, Qing Chen, Jing Gao, Latifur Khan, Jiawei Han and Bhavani Thuraisingham, "Classification and Novel Class Detection of Data Streams in a Dynamic Feature Space", Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, Volume 6322/2010, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010
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| 10 |
Zliobaite I., "Adaptive Training Set Formation", PhD Thesis, Vilnius University, 2010. |
| 11 |
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| 12 |
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| 13 |
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| 14 |
Žliobaitė,I.;Gabrys,B.; (2012) Adaptive Preprocessing for Streaming Data, IEEE Transactions on Knowledge and Data Engineering, vol.PP, no.99, pp.1
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