Feature Selection for Short Text Classification using Wavelet Packet Transform

Text classification tasks suffer from curse of dimensionality due to large feature space. Short text data further exacerbates the problem due to their sparse and noisy nature. Feature selection thus becomes an important step in improving the classification performance. In this paper, we propose a novel feature selection method using Wavelet Packet Transform. Wavelet Packet Transform (WPT) has been used widely in various fields due to its efficiency in encoding transient signals. We demonstrate how short text classification task can be benefited by feature selection using WPT due to their sparse nature. Our technique chooses the most discriminating features by computing inter-class distances in the transformed space. We experimented extensively with several short text datasets. Compared to well known techniques our approach reduces the feature space size and improves the overall classification performance significantly in all the datasets.