[Lecture]Department of Statistics Professor Shaw-Hwa Lo,American Columbia University
The report theme: ¡°Interaction-based learning in big data: A method of Partitions and I-score"
Daty & Time: Jun.11 (Thursday), 2015, am 10:00.
Address: Yifu library 4404B, JiuLi campus
Speaker: Professor Shaw-Hwa Lo
We consider a computer intensive approach (Partition Retention (PR), Chernoff, Lo and Zheng (09)), based on an earlier method (Lo and Zheng (2002) for detecting which, of many potential explanatory variables, have an influence on a dependent variable Y. This approach is suited to detect influential variables in groups, where causal effects depend on the confluence of values of several variables. It has the advantage of avoiding a difficult direct analysis, involving possibly thousands of variables, guided by a measure of influence I. The main objective is to discover the influential variables, rather than to measure their effects. Once they are detected, the problem of dealing with a much smaller group of influential variables should be vulnerable to standard analysis. We are confining our attention to locating a few needles in a haystack.
The quality of variables selected is evaluated in two ways: first by classification error rates, then by functional relevance using external biological knowledge. We demonstrate that (1) the classification error rates can be significantly reduced by considering interactions; (2) incorporating interaction information into data analysis can be very rewarding in generating novel scientific findings. Heuristic explanations why and when the proposed methods may lead to such a dramatic (classification/ predictive) gain are briefly discussed.
Welcome all the faculty and students to come.