An Instance-based Plus Ensemble Learning Method for Classification of Scientific Papers

TL;DR

This paper presents a novel ensemble learning approach that combines instance-based methods and content/citation features for accurately classifying scientific papers into research fields, addressing the challenge of exponential publication growth.

Contribution

It introduces a new classification method that integrates seed papers, content and citation features, and ensemble techniques for scientific paper categorization.

Findings

Effective in categorizing papers into research areas

Utilizes both content and citation features

Demonstrates efficiency on DBLP datasets

Abstract

The exponential growth of scientific publications in recent years has posed a significant challenge in effective and efficient categorization. This paper introduces a novel approach that combines instance-based learning and ensemble learning techniques for classifying scientific papers into relevant research fields. Working with a classification system with a group of research fields, first a number of typical seed papers are allocated to each of the fields manually. Then for each paper that needs to be classified, we compare it with all the seed papers in every field. Contents and citations are considered separately. An ensemble-based method is then employed to make the final decision. Experimenting with the datasets from DBLP, our experimental results demonstrate that the proposed classification method is effective and efficient in categorizing papers into various research areas. We also find that both content and citation features are useful for the classification of scientific papers.