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Interpretable dimension reduction for classifying functional data

Tian, Tian Siva; James, Gareth M. - In: Computational Statistics & Data Analysis 57 (2013) 1, pp. 282-296

Classification problems involving a categorical class label Y and a functional predictor X(t) are becoming increasingly … Classification” (FAC), specifically designed for functional classification problems. FAC uses certain complexity constraints to … to other potential approaches in terms of both classification accuracy and model interpretability. …

Persistent link: https://www.econbiz.de/10011056555

Optimal feature selection for sparse linear discriminant analysis and its applications in gene expression data

Wang, Cheng; Cao, Longbing; Miao, Baiqi - In: Computational Statistics & Data Analysis 66 (2013) C, pp. 140-149

, demonstrating that TLDA is an optimal classification rule whose convergence rate is the best compared to existing methods. The …

Persistent link: https://www.econbiz.de/10010871430

Analysis of feature selection stability on high dimension and small sample data

Dernoncourt, David; Hanczar, Blaise; Zucker, Jean-Daniel - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 681-693

datasets but dealing with the same classification problem, do not overlap significantly. Although it is a crucial problem, few …

Persistent link: https://www.econbiz.de/10010871459

Estimating mutual information for feature selection in the presence of label noise

Frénay, Benoît; Doquire, Gauthier; Verleysen, Michel - In: Computational Statistics & Data Analysis 71 (2014) C, pp. 832-848

A way to achieve feature selection for classification problems polluted by label noise is proposed. The performances of …

Persistent link: https://www.econbiz.de/10010719660

Cluster Forests

Yan, Donghui; Chen, Aiyou; Jordan, Michael I. - In: Computational Statistics & Data Analysis 66 (2013) C, pp. 178-192

With inspiration from Random Forests (RF) in the context of classification, a new clustering ensemble method …

Persistent link: https://www.econbiz.de/10011056444

Sparse high-dimensional fractional-norm support vector machine via DC programming

Guan, Wei; Gray, Alexander - In: Computational Statistics & Data Analysis 67 (2013) C, pp. 136-148

This paper considers a class of feature selecting support vector machines (SVMs) based on Lq-norm regularization, where q∈(0,1). The standard SVM [Vapnik, V., 1995. The Nature of Statistical Learning Theory. Springer, NY.] minimizes the hinge loss function subject to the L2-norm penalty....

Persistent link: https://www.econbiz.de/10011056518

Variable selection by Random Forests using data with missing values

Hapfelmeier, A.; Ulm, K. - In: Computational Statistics & Data Analysis 80 (2014) C, pp. 129-139

Variable selection has been suggested for Random Forests to improve data prediction and interpretation. However, the basic element, i.e. variable importance measures, cannot be computed straightforward when there are missing values in the predictor variables. Possible solutions are multiple...

Persistent link: https://www.econbiz.de/10010906927

Time-efficient estimation of conditional mutual information for variable selection in classification

Todorov, Diman; Setchi, Rossi - In: Computational Statistics & Data Analysis 72 (2014) C, pp. 105-127

An algorithm is proposed for calculating correlation measures based on entropy. The proposed algorithm allows exhaustive exploration of variable subsets on real data. Its time efficiency is demonstrated by comparison against three other variable selection methods based on entropy using 8 data...

Persistent link: https://www.econbiz.de/10010730217

Plasmode simulation for the evaluation of pharmacoepidemiologic methods in complex healthcare databases

Franklin, Jessica M.; Schneeweiss, Sebastian; Polinski, … - In: Computational Statistics & Data Analysis 72 (2014) C, pp. 219-226

Longitudinal healthcare claim databases are frequently used for studying the comparative safety and effectiveness of medications, but results from these studies may be biased due to residual confounding. It is unclear whether methods for confounding adjustment that have been shown to perform...

Persistent link: https://www.econbiz.de/10010730220

A Bayesian mixture of lasso regressions with t-errors

Cozzini, Alberto; Jasra, Ajay; Montana, Giovanni; … - In: Computational Statistics & Data Analysis 77 (2014) C, pp. 84-97

The following article considers a mixture of regressions with variable selection problem. In many real-data scenarios, one is faced with data which possess outliers, skewness and, simultaneously, one would like to be able to construct clusters with specific predictors that are fairly sparse. A...

Persistent link: https://www.econbiz.de/10010871312