litlas
Log inCreate account

Quadratic Programming Feature Selection

Authors: Rodríguez-Luján, Irene, Elkan, Charles, Santa Cruz, Carlos, Huerta, Ramón - LA Referencia 2010 cited by 216

Identifying a subset of features that preserves classification accuracy is a problem of growing importance, because of the increasing size and dimensionality of real-world data sets. We propose a new feature selection method, named Quadratic Programming Feature Selection (QPFS), that reduces the task to a quadratic optimization problem. In order to limit the computational complexity of solving the optimization problem, QPFS uses the Nystr¨om method for approximate matrix diagonalization. QPFS is thus capable of dealing with very large data sets, for which the use of other methods is computationally expensive. In experiments with small and medium data sets, the QPFS method leads to classification accuracy similar to that of other successful techniques. For large data sets, QPFS is superior in terms of computational efficiency.

Classification andRegression Trees.Classification and Regression Trees.Wrappers for FeatureSubset SelectionWrappers for Feature Subset SelectionGradient-based learningapplied to document…Gradient-based learning applied to document recognitionStatistical PatternRecognition: A ReviewStatistical Pattern Recognition: A ReviewLIBSVM: A library forsupport vector machinesLIBSVM: A library for support vector machinesElements of InformationTheoryElements of Information TheoryMinimum RedundancyFeature Selection from…Minimum Redundancy Feature Selection from Microarray Gene Expression DataFeature Selection forHigh-Dimensional Data…Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter SolutionTheoretical andEmpirical Analysis of…Theoretical and Empirical Analysis of ReliefF and RReliefFFeature Selection Basedon Mutual Information…Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-RedundancyGene selection algorithmby combining reliefF an…Gene selection algorithm by combining reliefF and mRMRFeature Selection forGene Expression Using…Feature Selection for Gene Expression Using Model-Based EntropyFeature Selection viaL1-Penalized…Feature Selection via L1-Penalized Squared-Loss Mutual InformationHigh-Dimensional FeatureSelection by…High-Dimensional Feature Selection by Feature-Wise Kernelized LassoEffective globalapproaches for mutual…Effective global approaches for mutual information based feature selectionFeature Selection viaGlobal Redundancy…Feature Selection via Global Redundancy MinimizationCan high-orderdependencies improve…Can high-order dependencies improve mutual information based feature selection?Variational InformationMaximization for Featur…Variational Information Maximization for Feature SelectionMulti-label regularizedquadratic programming…Multi-label regularized quadratic programming feature selection algorithm with Frank-Wolfe methodA Pearson's correlationcoefficient based…A Pearson's correlation coefficient based decision tree and its parallel implementationUltra High-DimensionalNonlinear Feature…Ultra High-Dimensional Nonlinear Feature Selection for Big Biological DataDistributed multi-labelfeature selection using…Distributed multi-label feature selection using individual mutual information measuresDistributed Selection ofContinuous Features in…Distributed Selection of Continuous Features in Multilabel Classification Using Mutual InformationTwo-timescaleneurodynamic approaches…Two-timescale neurodynamic approaches to supervised feature selection based on alternative problem formulationsQuadratic ProgrammingFeature SelectionQuadratic Programming Feature SelectionEarlier referencesFocus paperCiting papersOlderNewer

Click a node to pin it, click the empty canvas to go back to this paper, or hover to preview. Open a node’s page from its title.