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Motivation
Early work in data mining did not address the complex circumstances in which models are built
and applied. It was assumed that a fixed amount of data was available and only simple objectives such
as predictive accuracy were considered. Over time, it became clear that these assumptions
were unrealistic and that various utility factors related to acquiring data, building models, and
applying models had to be considered. The machine learning and data mining communities responded
with research on active learning, which focused on methods for cost-effective acquisition of information
for the training data, and research on cost-sensitive learning, which considered the costs and benefits
associated with using the learned knowledge and how these costs and benefits should be factored into the
data mining process.
All the different stages of the data mining process impact the ultimate utility of the knowledge
derived from data mining. The utility of acquiring data, extracting a model, and applying the acquired
knowledge must be considered. For example, in the data acquisition phase the costs of obtaining informative
and accurate data may be considered to help identify the most cost-effective information. Similarly, utility
considerations also impact the assessment of the decisions made based on the learned knowledge. Simple
assessment measures like predictive accuracy have given way to economic utility measures, such as profitability
and return on investment.
Goals
As was the case for the first workshop, this
workshop will bring together researchers who currently
contribute to different utility aspects of the data mining process. Our goal is to promote
an examination of all of the utility
factors that affect data mining and their interaction, in order to continue to encourage
the field to go beyond what has been accomplished individually in the areas of active learning
and cost-sensitive learning. In addition, this workshop will continue to explore the types of utility factors and new methods for incorporating
utility considerations in both predictive and descriptive data mining tasks. We
welcome recent work on Value of Information analysis
over graphical models.
This workshop will focus on real world experiences as well as existing and new research methods and results.
Attendance is not limited to the paper authors and we strongly encourage interested researchers from related areas
to attend the workshop. This will be a full-day workshop and will include invited talks, paper presentations,
short position statements and two panel discussions.
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