Improving Paper Classification Using Forecasting

The number of scientific publications increases tremendously over the past decades. Classifying papers to arXiv style categories makes it easier for individuals to find relevant papers and improves the overall efficiency in the research community. However, the majority of papers are not categorized and manually classifying hundreds of millions of papers is prohibitive. Recent advances in machine learning have paved a way for automatic paper classification. In this paper, we propose an approach to further improve the classification accuracy. Our approach takes advantage of the big-data nature of the problem and uses the publication trend to forecast the number of papers for each category. Then we use the forecasted label distribution to revise the classification results returned by base classifiers. A data-driven heuristic approach is introduced to address the label revision problem. Our experiments with a large publicly available dataset demonstrated that the proposed solution improves the accuracy of all existing approaches.