Identifying Rising Stars via Supervised Machine Learning

Identifying rising stars is very useful for faster growth of any organization. Rising entities has been explored in academics, sports, and blogs in the recent past, but business side is ignored. However, predicting rising business managers (RBMs) can result in significant business growth of any business. In order to maintain a competitive edge, machine learning techniques should be adopted to devise intelligent business strategies and perform predictions. In this article, RBMs are classified by exploring features of co-business managers (Co-BMs), rather than their own work history. Since ignoring their work history enables such prediction in a cold-start scenario, where work history is not available. After formulating features of Co-BM, the dataset is classified into two different evaluation setups. One is average revenue (AR) and the other one is average relative increase in revenue (ARIR)—class labels. All instances for both labels are randomly sorted into multisize (10, 20, and 30–100) datasets. Later on, these datasets are explored through machine learning classifiers using fivefold cross validation. In order to compare the prediction results with baseline and to measure the effectiveness of the proposed methods, the candidates’ business scores are used, which are calculated by the business definition and formulation. In terms of precision, recall, and f-measure, the feature, category, and model-based experimental results show that the generative models, particularly Bayesian networks, produce better results for an AR-based dataset. Also, overall results show the effective the proposed method.