Campus Event Calendar
Event Entry
What and Who
Data Discretization Unification
Yuri Breitbart
Kent State University
Talk
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, RG2
Expert Audience
English
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Date, Time and Location
Wednesday, 11 July 2007
10:15
-- Not specified --
E1 4
024
Saarbrücken
Abstract
Data discretization is defined as a process of converting continuous
data attribute values into a finite set of intervals with minimal loss of
information. In this talk, we prove that discretization methods based on
informational theoretical complexity and the methods based on statistical
measures of data dependency are asymptotically equivalent. Furthermore, we
define a notion of generalized entropy and prove that discretization
methods based on MDLP, Gini Index, AIC, BIC, Pearson's X_2, and Wilks' G_2
statistics are all derivable from the generalized entropy function. We
design a dynamic programming algorithm that guarantees the best
discretization based on the generalized entropy notion. Furthermore, we
conducted an extensive performance evaluation of our method for several
publicly available data sets. Our results show that our method delivers on
the average 31% less classification errors than many previously known
discretization methods.
This is a joint work with Ruoming Jin and Chibuike Muoh from Kent State
University.
data attribute values into a finite set of intervals with minimal loss of
information. In this talk, we prove that discretization methods based on
informational theoretical complexity and the methods based on statistical
measures of data dependency are asymptotically equivalent. Furthermore, we
define a notion of generalized entropy and prove that discretization
methods based on MDLP, Gini Index, AIC, BIC, Pearson's X_2, and Wilks' G_2
statistics are all derivable from the generalized entropy function. We
design a dynamic programming algorithm that guarantees the best
discretization based on the generalized entropy notion. Furthermore, we
conducted an extensive performance evaluation of our method for several
publicly available data sets. Our results show that our method delivers on
the average 31% less classification errors than many previously known
discretization methods.
This is a joint work with Ruoming Jin and Chibuike Muoh from Kent State
University.
Contact
Gerhard Weikum
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Petra Schaaf, 06/25/2007 09:51
Petra Schaaf, 06/18/2007 11:27 -- Created document.
Petra Schaaf, 06/18/2007 11:27 -- Created document.