Apologies for spamming your mailbox.

Please note that the talk is now moved to room ENGR 2901
starting at 3PM today.

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*    GRAND Seminar
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*    http://cs.gmu.edu/~robotics/Main/GrandSeminar
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*Title*

White-box Data Mining Algorithms


*Time/Venue*

ENGR 2901
3PM, March 26, Monday


*Speaker*

Boris Delibasic
Associate professor
University of Belgrade in Serbia

*Host*

Alex Brodsky

*Abstract*

Choosing the right algorithm for data at hand was always a major
problem in data mining. We propose a new architecture for
decision-support systems for data mining, with the ability of generic
algorithm design to help users choose the right algorithm. Opposite to
the prevalent black-box approach of using algorithms in data mining
were users have the ability to define inputs, setup parameters and
read outputs, we propose using reusable component (RC) based
algorithms. The RC-based algorithms are assembled from reusable
components, which are standalone algorithm units which were originally
found in black-box algorithms and their partial improvements. RC based
algorithms have been proven to better adapt to data than black-box
algorithms that, due to “hard” bindings of algorithm parts, are
disabled to achieve best results on some datasets. On the other hand,
the RC-based approach of algorithm design produces a galore of
algorithms making it thus harder to search through the algorithm
space. We show how this problem can be solved using meta-heuristics
for searching through the algorithm space. We also propose further
research directions that will enable to connect the proposed approach
with meta-learning. We believe that users will be better supported in
the future for choosing an adequate algorithm for the problem at hand,
because the decision support system will be enabled to perform an
intelligent search through the algorithm space that is based on
dataset properties, algorithm performance results, empirical rules
gained from meta-learning and theoretical support.

Short bio:

Boris Delibasic is an associate professor at the University of
Belgrade in Serbia. His main research interests are data mining,
decision support systems, business intelligence, and decision theory.
Dr. Delibasic is also an adjunct lecturer at the University of Jena in
Germany. He has already published several research articles in
top-ranked international journals. A project he is currently engaged
with is dealing with design of white-box algorithms for data mining
(www.whibo.fon.bg.ac.rs). In 2011, Prof. Delibasic received a
prestigious Fulbright fellowship to work as a visiting scholar at
Zoran Obradovic’s Center for data analytics and biomedical informatics
at Temple University in Philadelphia, PA. His current research
objectives are to design spatio-temporal algorithms for analysis of
ski injuries and to discover ski injury patterns that could be used
for injury prevention. Algorithms developed for ski injury analysis,
are planned in a later stage to be extended, to analyze large scale
data on road traffic accidents. Dr. Delibasic is also ski patroller on
Serbian mountains during the winter season.

-- 
*Jyh-Ming Lien*
Assistant Professor, George Mason University
+1-703-993-9546
http://cs.gmu.edu/~jmlien