Document Classification of New York Times Articles
Input data is related to the documents of New York Times (2000 - 2003) Each of the documents are related to one the following categories: News, Opinion, Classified, Features A Classifier is developed here to classify the category of the articles based their context.
Fearure Extraction Methods:
- Bag of Words
- TF_IDF
- Dimension Reduction
Classification Algorithms:
- SVM (Gaussian, Linear, and Sigmoid Kernel)
- Neural Networks (MLP)
Cross validation is used for tuning the parameters of each method.
Results of the Implementation:
| Classifier \ Feature extraction | tf_idf(1vsAll) | bagging(1vsAll) | Tf_idf (reduced dim) | Bagging(reduced dim) | tf_idf | bagging |
|---|---|---|---|---|---|---|
| SVM RBF kernel | 85.01 | 83.58 | 61.15 | 55.55 | 65.95 | 66.99 |
| SVM linear kernel | 83.21 | 82.68 | 23.5 | 23.5 | 61.2 | 66.05 |
| SVM sigmoid kernel | 85.01 | 71.6 | 61.2 | 56.3 | 64.7 | 66.75 |
| NN MLP | - | - | 49.9 | 50.6 | - | - |
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