A Generic Ranking Service on Scientific Datasets
Mojgan Ghanavati, Raymond K. Wong, Fang Chen, Yang Wang · 2015
Different ranking algorithms have been proposed to fulfil the need of ranking. The problem is that most of the existing algorithms and models are just applicable on a specific data. When the data is imbalanced and heterogeneous, finding the records belonging to the minority class is significant especially in failure cases. So considering ranking as a classification problem of predicting the specific relevance score for any category, we are going to propose a generic ranking service. In this model, a metric learning based ranking model is proposed which can be used on wide range of scientific data sets. A real world imbalanced and heterogeneous data set is used to prove the efficiency of model.