Abstract
In this paper, the feature selection for classification of natural disaster texts through testors, is presented. Testors are features subsets such that no class confusion is introduced. Typical testors are irreducible testors. Then they can be used in order to select which words are relevant to separate the classes, and so, be useful to get better classification rates. Some experiments were done with KNN and Naive Bayes Classifiers, results were compared against frequency threshold and information gain methods.
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Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. (2004). Feature Selection for Natural Disaster Texts Classification Using Testors. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_62
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DOI: https://doi.org/10.1007/978-3-540-28651-6_62
Publisher Name: Springer, Berlin, Heidelberg
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