Marina Sokolova works in Text Data Mining and Machine Learning. She is a faculty member at University of Ottawa, Faculty of Medicine and School of Electrical Engineering and Computer Science. Dr. Sokolova obtained her PhD from the School of Information Technology and Engineering, University of Ottawa in 2006. She has been awarded with grants and merit-based awards from Natural Sciences and Engineering Research Council, Canadian Institutes of Health Research, and Japan Society for the Promotion of Science.
Social Mining is an emerging field of Data Mining that focuses on discovery of social phenomena in data collected from personal records. In traditional data mining studies, Machine Learning techniques are routinely used to predict patient diagnosis, client credit scores, consumer satisfaction with retail and services.
In this talk we consider a reverse problem, i.e. classification demographic characteristics from data sets of personal records. In other words, we look at how Machine Learning can analyze personal data from the perspective of Social Mining. We apply Multi-Label Classification techniques on data sets collected in health care, banking and financial settings. We will also discuss how data collection routines can affect the obtained results.
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