[Intelligence Seminar] November 10: Carla Brodley, GHC 4303, 3:30, "Challenges in the Practical Application of Machine Learning"

[Noah A Smith]

Intelligence Seminar

November 10, 2009
3:30 pm
GHC 4303
Host:  Manuela Veloso
For meetings, contact Dana Houston (dhouston at cs.cmu.edu)


Challenges in the Practical Application of Machine Learning
Carla E. Brodley, Tufts University

Abstract:

In this talk I will discuss the factors that impact the successful
application of supervised machine learning.  Driven by several
interdisciplinary collaborations, we are addressing the
problem of what to do when your your initial accuracy is lower than is
acceptable to your domain experts.  Low accuracy can be due to three
factors: noise in the class labels, insufficient training data, and
whether the features describing each training example are able to
discriminate the classes.  In this talk, I will discuss research
efforts at Tufts addressing the second two factors.  The first
project, introduces a new problem which we have named active class
selection (ACS).  ACS arises when one can ask the question: given the
ability to collect n additional training instances, how should they be
distributed with respect to class?  The second project examines how
one might assess that the class distinctions are not supported by the
features and how constraint-based clustering can be used to uncover
the true class structure of the data.  These two issues and their
solutions will be explored in the context of three applications.  The
first is to create a map of global map of the land cover of the
Earth's surface from remotely sensed data (satellite data).  The
second is to build a classifier based on data collected from an
"artificial nose" to discriminate vapors.  The "nose" is a collection
of sensors that have different reactions to different vapors. The
third is to classify HRCT images of the lung.

Bio:

Carla E. Brodley is a professor in the Department of Computer Science
at Tufts University. She received her PhD in computer science from the
University of Massachusetts, at Amherst in 1994. From 1994-2004, she
was on the faculty of the School of Electrical Engineering at Purdue
University. Professor Brodley's research interests include machine
learning, knowledge discovery in databases, and computer security. She
has worked in the areas of anomaly detection, active learning,
classifier formation, unsupervised learning, and applications of
machine learning to remote sensing, computer security, digital
libraries, astrophysics, content-based image retrieval of medical
images, computational biology, saliva diagnostics, evidence-based
medicine and chemistry. She was a member of the DSSG in 2004-2005. In
2001 she served as program co-chair for the International Conference
on Machine Learning (ICML) and in 2004, she served as the general
chair for ICML. Currently she is an associate editor of JMLR and
Machine Learning, and she is on the editorial board of DKMD.
She is a member of the AAAI Council and is co-chair of the
Computing Research Association's Committee on the Status of Women in
Computing Research (CRA-W).