  In this work, a problem associated with imbalanced text corpora is addressed.
A method of converting an imbalanced text corpus into a balanced one is
presented. The presented method employs a clustering algorithm for conversion.
Initially to avoid curse of dimensionality, an effective representation scheme
based on term class relevancy measure is adapted, which drastically reduces the
dimension to the number of classes in the corpus. Subsequently, the samples of
larger sized classes are grouped into a number of subclasses of smaller sizes
to make the entire corpus balanced. Each subclass is then given a single
symbolic vector representation by the use of interval valued features. This
symbolic representation in addition to being compact helps in reducing the
space requirement and also the classification time. The proposed model has been
empirically demonstrated for its superiority on bench marking datasets viz.,
Reuters 21578 and TDT2. Further, it has been compared against several other
existing contemporary models including model based on support vector machine.
The comparative analysis indicates that the proposed model outperforms the
other existing models.
