Abstract:
Automatic categorization of text document
is difficult and time consuming. This thesis
proposed a method for automatic categorization
of computer science paper using random forest
classifier. Decision trees are widely used for the
text categorization task and random forest offers
the high accuracy and due to the nature of
random forest, it is suitable for the task of text
categorization.Random forest used different
training dataset and random split at each node
for the construction of decision forest.Using
random forest can provide high accuracy in
categorization of text documents.The system will
be implemented using C# programming language
MS SQL server 2005. Computer sciences paper
from IEEE conferences will be collected and
trained using the random forest. Trained random
forest will be stored using C# serialization
technique in hard disk. Stored random forest can
be used to classify the incoming conference paper
into their associated category. The proposed
system will compute the accuracy on the test
dataset using hold-out method and will compare
the accuracy with decision tree (C4.5) algorithm.
