Statistics Colloquium
November 8 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower
A Novel Ensemble Technique using Rank Aggregation for Mining Complex High Dimensional Data
Susmita Datta
President of Caucus for Women in Statistics, 2013
Professor, Graduate Director and Distinguished University Scholar
Department of Bioinformatics and Biostatistics
University of Louisville
Data mining techniques such as clustering and classification are
extremely important in analyzing high dimensional and high throughput
biomedical data such as microarray mRNA/miRNA and mass spectrometry
data. However, due to the availability of many clustering and classification
algorithms the obtained results are not the same across all such
techniques used to analyze the same data. This poses a problem to the
scientific world. Hence, we have developed an ensemble method to unify
the results of all such data mining algorithms. This ensemble technique
is formed by a stochastic optimization, rank aggregation technique
using a Monte Carlo cross-entropy algorithm. We show that this ensemble
technique can be used with all possible clustering and classification
algorithms. We provide a brief description of the open source R based
software and provide analysis of some real life microarray data to
demonstrate our method.
All statistics students are expected to attend.
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