Feature selection for linear SVMs under uncertain data: Robust optimization based on difference of convex functions algorithms

In this paper, we consider the problem of feature selection for linear SVMs on uncertain data that is inherently prevalent in almost all datasets. Using principles of Robust Optimization, we propose robust schemes to handle data with ellipsoidal model and box model of uncertainty. The difficulty in...

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Vydáno v:Neural networks Ročník 59; s. 36 - 50
Hlavní autoři: Le Thi, Hoai An, Vo, Xuan Thanh, Pham Dinh, Tao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Kidlington Elsevier Ltd 01.11.2014
Elsevier
Témata:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Computer Science
Computer science; control theory; systems
Data Interpretation, Statistical
Data processing. List processing. Character string processing
DC programming
DCA
Decision theory. Utility theory
Exact sciences and technology
Feature selection
Humans
Leukemia - genetics
Linear Models
Memory organisation. Data processing
Microarray Analysis
Operational research and scientific management
Operational research. Management science
Reliability theory. Replacement problems
Robust optimization
Software
Support Vector Machine
SVM
Theoretical computing
Weather
DC programming
Feature selection
Robust optimization
DCA
SVM
Difference of convex functions
Modeling
Selection problem
Uncertain system
Vector support machine
Selection criterion
Robustness
Data models
Ellipsoid
Mathematical programming
ISSN:0893-6080, 1879-2782, 1879-2782
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Shrnutí:In this paper, we consider the problem of feature selection for linear SVMs on uncertain data that is inherently prevalent in almost all datasets. Using principles of Robust Optimization, we propose robust schemes to handle data with ellipsoidal model and box model of uncertainty. The difficulty in treating ℓ0-norm in feature selection problem is overcome by using appropriate approximations and Difference of Convex functions (DC) programming and DC Algorithms (DCA). The computational results show that the proposed robust optimization approaches are superior than a traditional approach in immunizing perturbation of the data.
Bibliografie:ObjectType-Article-1
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ISSN:0893-6080
1879-2782
1879-2782
DOI:10.1016/j.neunet.2014.06.011