# A Sparse Signomial Model for Classification and Regression

Event Details
• Date/Time:
• Friday February 12, 2010 - Saturday February 13, 2010
11:00 am - 11:59 am
• Location: ISyE Executive classroom
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Summary Sentence: A Sparse Signomial Model for Classification and Regression

Full Summary: A Sparse Signomial Model for Classification and Regression

TITLE: A Sparse Signomial Model for Classification and Regression

SPEAKER: Professor Myong K. (MK) Jeong

ABSTRACT:

Support Vector Machine (SVM) is one of the most popular data mining tools for solving classification and regression problems. Due to its high prediction accuracy, SVM has been successfully used in various fields. However, SVM has the following drawbacks.  First, it is not easy to get an explicit description of the discrimination (or regression) function in the original input space and to make a variable selection decision in the input space. Second, depending on the magnitude and numeric range of the given data points, the resulting kernel matrices may be ill-conditioned, so learning algorithms may be suffered from numerical instability even though data scaling generally helps to handle this kind of issues but may not be always effective. Third, the selection of an appropriate kernel type and its parameters can be complex while the performance of the resulting functions is heavily influenced.

To overcome these drawbacks, this talk presents the sparse signomial classification and regression (SSCR) model. SSCR seek a sparse signomial function by solving a linear program to minimize the weighted sum of the 1-norm of the coefficient vector of the function and the 1-norm of violation (or loss) caused by the function. SSCR can explorevery high demensional feature spaces with less sensitivity to numerical values or numeric ranges of the given data. Moreover, this method give an explicit description of the resulting function in the original input space, which can be used for prediction purposes as well as interpretation purposes. We present a practical implementation of SSCR based on the column generation and explore some theoretical properties of the proposed formulation. Computational study shows that SSCR is competitive or even better performance compared to other widely used learning methods for classification and regression.

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School of Industrial and Systems Engineering (ISYE)

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Seminar/Lecture/Colloquium
Keywords
SSCR, SVM
Status
• Created By: Anita Race
• Workflow Status: Published
• Created On: Feb 3, 2010 - 7:30am
• Last Updated: Oct 7, 2016 - 9:49pm