Clustering Based Feature Selection using Extreme Learning Machines for Text Classification

The expansion of the dynamic Web increases the digital documents, which has attracted many researchers to work in the field of text classification. It is an important and well studied area of machine learning with a variety of modern applications. A good feature selection is of paramount importance to increase the efficiency of the classifiers working on text data. Choosing the most relevant features out of what can be an incredibly large set of data, is particularly important for accurate text classification. This paper is a motivation in that direction where we propose a new clustering based feature selection technique that reduces the feature size. Traditional k-means clustering technique along with TF-IDF and Wordnet helps us to form a quality and reduced feature vector to train the Extreme Learning Machine (ELM) and Multi-layer ELM (ML-ELM) which have been used as the classifiers for text classification. The experimental work has been carried out on 20-Newsgroups and DMOZ datasets. Results on these two standard datasets demonstrate the efficiency of our approach using ELM and ML-ELM as the classifiers over the state-of-the-art classifiers.