Why is SVM best for text classification?
2.1 Introduction to SVM Support Vector Machines can solve non-linear classification problems: data, using a “kernel” function, is mapped to a vector space with a greater number of dimensions so that the original problem can be redefined as a problem having characteristics of linear separability.
How can we categorize data?
An organization may classify data as Restricted, Private or Public. In this instance, public data represents the least-sensitive data with the lowest security requirements, while restricted data is in the highest security classification and represents the most sensitive data.
How can I improve my text classification model?
The performance of a text classification model is heavily dependent upon the type of words used in the corpus and type of features created for classification. I used several practices to improve the results of my model.
How are text classifiers used in text classification?
Text classification (a.k.a. text categorization or text tagging) is the task of assigning a set of predefined categories to open-ended text. Text classifiers can be used to organize, structure, and categorize pretty much any kind of text – from documents, medical studies and files, and all over the web.
What happens when you add more features to a classifier?
As more features are added, the classifier has a higher chance to find a hyperplane to split the data. However, if we increase the dimensionality without increasing the number of training samples, the feature space becomes more sparse and the classifier overfits easily.
Which is the best CV for text classification?
Section 2 of the paper contains more details. Baseline performance: The authors used 10-fold CV on a randomly sampled 15k dataset (balanced). The best results they achieved were with RBF-SVM achieving an accuracy of 93%, Precision 0.95, Recall 0.9, F1 of 0.93, ROC-AUC of 0.97