An empirical study on various text classifiers

Text classification has gained importance more than ever in the present day owing to the huge amount of data generated with the advent of technology. There are a numerous well established techniques available to achieve classification. It is difficult to declare an algorithm to be universally efficient over the huge variety of datasets created in real time. In this paper, the existing methods are compared and contrasted based on experimental results. The experiment involves testing a document against the training set created previously. The results show quantitative values of the comparable parameters and hence helpful in the choice of a classification algorithm.