Performance Analysis of Abstract based Classification of Medical Journals using Ensemble Methods

Abstract

Fetching the suitable web-based literature is one of the leading issues faced by the researchers now-a-days. This is due to the increase in number of articles with lack of appropriate keyword searching procedures. Specifically, in the field of biomedical research, the scientific literature repositories are growing exponentially. So, getting the appropriate and relevant documents from the vast amount of biomedical literatures is one of the key issues. To tackle the issue this research work proposed a model that uses topic modelling algorithm as the prior step before the implementation of ensemble algorithms to efficiently do the automatic labeling and classification respectively. National Center for Biotechnology Information’s (NCBI) - PubMed is the biggest source of peer-reviewed biological literatures for researchers and health practitioners in the field of biomedical. The dataset is collected from the NCBI’s Pubmed website. In this proposed work, we implemented and evaluated the performance of existing multiple classifier ensemble algorithms such as Bagging and Boosting. In this analysis, we have found that the Decision Tree algorithm performs well than other models.