A Machine Learning Based Approach to Categorize Research Journals

TL;DR

This paper presents a hybrid machine learning model to categorize and predict research journals, improving the process of journal classification using feature selection, clustering, and ensemble techniques.

Contribution

It introduces a novel hybrid predictive approach combining clustering, feature selection, and ensemble methods for journal categorization and prediction.

Findings

The model achieved high accuracy and precision in journal classification.

Feature selection improved the model's effectiveness.

The approach aids researchers in predicting journal categories effectively.

Abstract

In this modern technological era, categorization and ranking of research journals is gaining popularity among researchers and scientists. It plays a significant role for publication of their research findings in a quality journal. Although, many research works exist on journal categorization and ranking, however, there is a lack of research works to categorize and predict the journals using suitable machine learning techniques. This work aims to categorize and predict various academic research journals. This work suggests a hybrid predictive model comprising of five steps. The first step is to prepare the dataset with twenty features. The second step is to pre-process the dataset. The third step is to apply an appropriate clustering algorithm for categorization. The fourth step is to apply appropriate feature selection techniques to get an effective subset of features. The fifth step involves some ensemble plus non ensemble methods to train the model. The model is trained on a full set of features, and a selected subset of features is obtained by applying three feature selection techniques. After model training, the prediction results are evaluated in terms of precision, recall, and accuracy. The results can help the researchers and the practitioners in predicting the journal category.