608 research outputs found
Support vector machines with constraints for sparsity in the primal parameters
This paper introduces 1 a new support vector machine (SVM) formulation to obtain sparse solutions in the primal SVM parameters, providing a new method for feature selection based on SVMs. This new approach includes additional constraints to the classical ones that drop the weights associated to those features that are likely to be irrelevant. A !-SVM formulation has been used, where ! indicates the fraction of features to be considered. This paper presents two versions of the proposed sparse classifier, a 2-norm SVM and a 1-norm SVM, the latter having a reduced computational burden with respect to the first one. Additionally, an explanation is provided about how the presented approach can be readily extended to multiclass classification or to problems where groups of features, rather than isolated features, need to be selected. The algorithms have been tested in a variety of synthetic and real data sets and they have been compared against other state of the art SVM-based linear feature selection methods, such as 1-norm SVMand doubly regularized SVM. The results show the good feature selection ability of the approaches.This work was supported in part by the Ministry of Science and
Innovation (Spanish Goverment), under Grant TEC2008-02473Publicad
Domain Knowledge integrated for Blast Furnace Classifier Design
Blast furnace modeling and control is one of the important problems in the
industrial field, and the black-box model is an effective mean to describe the
complex blast furnace system. In practice, there are often different learning
targets, such as safety and energy saving in industrial applications, depending
on the application. For this reason, this paper proposes a framework to design
a domain knowledge integrated classification model that yields a classifier for
industrial application. Our knowledge incorporated learning scheme allows the
users to create a classifier that identifies "important samples" (whose
misclassifications can lead to severe consequences) more correctly, while
keeping the proper precision of classifying the remaining samples. The
effectiveness of the proposed method has been verified by two real blast
furnace datasets, which guides the operators to utilize their prior experience
for controlling the blast furnace systems better.Comment: 9 pages, 4 figure
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