Automatic Classification of Research Papers to a Predefined Category Using Machine Learning - Noronha, Perpetua F; Prathiba, R; Gauthami, M (International Journal of Computer Sciences and Engineering, 2025)
With the technology growing exponentially, there are a lot of researches and inventions taking place in all the fields. New innovations and discoveries are put forth in the form of research papers. There are thousands of research papers today that pertain to different disciplines such as Computer Science, Mathematics, Biology, Chemistry etc. Finding papers pertaining to a specific domain is time consuming and a tedious task. Classification of papers to a specific discipline, subject or a category reduces the task of searching. This task if done manually consumes lot of human effort and time where as if done automatically, saves the time of users by preventing them from going through the entire research paper. The proposed work uses a novel strategy to automatically classify the research papers based by analyzing the structure of abstracts of research papers to assign them to a specific predefined discipline. Machine Learning technique is used to build a learning model to learn the properties or characteristics of documents manually, in some cases semi automatically, so that the more it gets trained the more efficient will be the model to predict or classify the test documents. Support Vector Machine (SVM) algorithm is used to vectorize the training data set and plot them in an n-dimensional space and then to find the hyper plane that will separate the data into a predefined category. The data is then learnt and later used to categorize the data. The performance of SVM is compared with Naïve Bayes and Decision Tree algorithms also. The experimental result proves the outstanding performance of SVM to predict the category of research papers over the other two algorithms mentioned above. The main objective of the proposed work is to develop a system that has the ability to learn from a training set of data, improvise from the experiences without explicitly programming for it and later classify any research paper given to it into a discipline.