Predicting the h-index with cost-sensitive naive Bayes

Bibliometric indices are an increasingly important topic for the scientific community nowadays. One of the most successful bibliometric indices is the well-known h-index. In view of the attention attracted by this index, our research is based on the construction of several prediction models to forecast the h-index of Spanish professors (with a permanent position) for a four-year time horizon. We built two different types of models (junior models and senior models) to differentiate between professors' seniority. These models are learnt from bibliometric data using a cost-sensitive naive Bayes approach that takes into account the expected cost of instances predictions at classification time. Results show that it is easier to predict the h-index of the one-year time horizon than the others, that is, it has a higher average accuracy and lower average total cost than the others. Similarly, it is easier to predict the h-index of junior professors than senior professors.