Abstract
We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases the accuracy at the same time. The test example is classified using simpler and smaller model. The training examples in a particular cluster share the common vocabulary. At the time of clustering, we do not take into account the labels of the training examples. After the clusters have been created, the classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. Our model also outperforms A Decision Cluster Classification (ADCC) and the Decision Cluster Forest Classification (DCFC) models on the Reuters-21578 dataset.
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Notes
- 1.
The terms categorization and classification will be used interchangeably throughout the paper.
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Nizamani, S., Memon, N., Wiil, U.K. (2011). Cluster Based Text Classification Model. In: Wiil, U.K. (eds) Counterterrorism and Open Source Intelligence. Lecture Notes in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0388-3_14
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