Feature Selection Filters Based on the Permutation Test

Radivojac, Predrag; Obradović, Zoran; Dunker, A. Keith; Vučetić, Slobodan

doi:10.1007/978-3-540-30115-8_32

Cited by 40 publications

(37 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Feature filtering with the mutual information and the permutation test was also recently proposed [6,28,26], in a pure feature ranking approach where the permutation test is used to automatically set a threshold on the value of the mutual information.…”

Section: Combined Usesmentioning

confidence: 99%

Resampling methods for parameter-free and robust feature selection with mutual information

et al. 2007

View full text Add to dashboard Cite

Combining the mutual information criterion with a forward feature selection strategy offers a good trade-off between optimality of the selected feature subset and computation time. However, it requires to set the parameter(s) of the mutual information estimator and to determine when to halt the forward procedure. These two choices are difficult to make because, as the dimensionality of the subset increases, the estimation of the mutual information becomes less and less reliable. This paper proposes to use resampling methods, a K-fold cross-validation and the permutation test, to address both issues. The resampling methods bring information about the variance of the estimator, information which can then be used to automatically set the parameter and to calculate a threshold to stop the forward procedure. The procedure is illustrated on a synthetic dataset as well as on real-world examples.

show abstract

Section: Combined Usesmentioning

confidence: 99%

Resampling methods for parameter-free and robust feature selection with mutual information

et al. 2007

View full text Add to dashboard Cite

show abstract

“…Because of these reasons, the variable importance statistical framework introduced by Breiman (2001), formalized in van der Laan (2006), and improved by Radivojac et al (2004) is what we will use in this paper.…”

Section: A Variable Importancementioning

confidence: 99%

Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?

Lakshmanan

Karstens

Krause

et al. 2015

Journal of Atmospheric and Oceanic Technology

View full text Add to dashboard Cite

Recently, a radar data quality control algorithm has been devised to discriminate between weather echoes and echoes due to nonmeteorological phenomena, such as bioscatter, instrument artifacts, and ground clutter (Lakshmanan et al.), using the values of polarimetric moments at and around a range gate. Because the algorithm was created by optimizing its weights over a large reference dataset, statistical methods can be employed to examine the importance of the different variables in the context of discriminating between weather and no-weather echoes. Among the variables studied for their impact on the ability to identify and censor nonmeteorological artifacts from weather radar data, the method of successive permutations ranks the variance of Zdr, the reflectivity structure of the virtual volume scan, and the range derivative of the differential phase on propagation [PhiDP (Kdp)] as the most important. The same statistical framework can be used to study the impact of calibration errors in variables such as Zdr. The effects of Zdr calibration errors were found to be negligible.

show abstract

“…The features were reduced to an "optimal" set using forward selection [25], which consists essentially of ranking the features in order of relevance to the classification problem and evaluating the performance of the classifier using an increasing number of features. We computed the relevance of each feature using the information gain ratio [25] metric, which has been found to be very reliable in previous applications [26]. However, the features need to be discretized; Fayyad and Irani's minimum description length algorithm [27] was used for this.…”

Section: ) Optimal Feature Set Selectionmentioning

confidence: 99%

Learning-Based Segmentation Framework for Tissue Images Containing Gene Expression Data

Bello

Carson

et al. 2007

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Associating specific gene activity with functional locations in the brain results in a greater understanding of the role of the gene. To perform such an association for the more than 20 000 genes in the mammalian genome, reliable automated methods that characterize the distribution of gene expression in relation to a standard anatomical model are required. In this paper, we propose a new automatic method that results in the segmentation of gene expression images into distinct anatomical regions in which the expression can be quantified and compared with other images. Our contribution is a novel hybrid atlas that utilizes a statistical shape model based on a subdivision mesh, texture differentiation at region boundaries, and features of anatomical landmarks to delineate boundaries of anatomical regions in gene expression images. This atlas, which provides a common coordinate system for internal brain data, is being used to create a searchable database of gene expression patterns in the adult mouse brain. Our framework annotates the images about four times faster and has achieved a median spatial overlap of up to 0.92 compared with expert segmentation in 64 images tested. This tool is intended to help scientists interpret large-scale gene expression patterns more efficiently.

show abstract

Feature Selection Filters Based on the Permutation Test

Cited by 40 publications

References 31 publications

Resampling methods for parameter-free and robust feature selection with mutual information

Resampling methods for parameter-free and robust feature selection with mutual information

Which Polarimetric Variables Are Important for Weather/No-Weather Discrimination?

Learning-Based Segmentation Framework for Tissue Images Containing Gene Expression Data

Contact Info

Product

Resources

About