Quantitative Issues in Cancer Research Working Group Seminar
Omar Melikechi, Postdoctoral Research Fellow, Department of Biostatistics, Harvard University
Integrated path stability selection
Abstract: Feature selection aims to identify important features in a data set, which can lead to more accurate and interpretable results. For example, it has been used to identify genes that are associated with certain diseases. Stability selection is a popular method for improving feature selection algorithms. However, it often selects few features, resulting in a low true positive rate. In this talk, I will introduce a novel approach to stability selection, called integrated path stability selection (IPSS), that yields significantly more true positives in practice, while still controlling the number of false positives. Furthermore, IPSS is fast, effective in high dimensions, and easy to implement, requiring just one user-specified parameter: the target number of false positives or the target false discovery rate. After introducing the method, I will demonstrate its performance on cancer data.