Title :
A Generic Ranking Service on Scientific Datasets
Author :
Ghanavati, Mojgan ; Wong, Raymond K. ; Fang Chen ; Yang Wang
Author_Institution :
Sch. of Comput. Sci., Univ. of New South Wales, Sydney, NSW, Australia
Abstract :
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.
Keywords :
learning (artificial intelligence); pattern classification; classification problem; generic ranking service; heterogeneous data set; metric learning based ranking model; real world imbalanced data set; scientific datasets; Australia; Data models; Measurement; Pipelines; Prediction algorithms; Predictive models; Support vector machines; Classification; Local Metric Learning; Ranking Service; SVM;
Conference_Titel :
Services Computing (SCC), 2015 IEEE International Conference on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7280-0
DOI :
10.1109/SCC.2015.73
