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Selection: with tag cross-validation [7 articles] 

 

Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure

  
Ecography, Vol. 40, No. 8. (1 August 2017), pp. 913-929, https://doi.org/10.1111/ecog.02881

Abstract

Ecological data often show temporal, spatial, hierarchical (random effects), or phylogenetic structure. Modern statistical approaches are increasingly accounting for such dependencies. However, when performing cross-validation, these structures are regularly ignored, resulting in serious underestimation of predictive error. One cause for the poor performance of uncorrected (random) cross-validation, noted often by modellers, are dependence structures in the data that persist as dependence structures in model residuals, violating the assumption of independence. Even more concerning, because often overlooked, is that structured data also ...

 

An empirical comparison of model validation techniques for defect prediction models

  
IEEE Transactions on Software Engineering, Vol. 43, No. 1. (1 January 2017), pp. 1-18, https://doi.org/10.1109/tse.2016.2584050

Abstract

Defect prediction models help software quality assurance teams to allocate their limited resources to the most defect-prone modules. Model validation techniques, such as k -fold cross-validation, use historical data to estimate how well a model will perform in the future. However, little is known about how accurate the estimates of model validation techniques tend to be. In this paper, we investigate the bias and variance of model validation techniques in the domain of defect prediction. Analysis of 101 public defect datasets ...

 

Resampling methods for meta-model validation with recommendations for evolutionary computation

  
Evolutionary Computation, Vol. 20, No. 2. (16 February 2012), pp. 249-275, https://doi.org/10.1162/evco_a_00069

Abstract

Meta-modeling has become a crucial tool in solving expensive optimization problems. Much of the work in the past has focused on finding a good regression method to model the fitness function. Examples include classical linear regression, splines, neural networks, Kriging and support vector regression. This paper specifically draws attention to the fact that assessing model accuracy is a crucial aspect in the meta-modeling framework. Resampling strategies such as cross-validation, subsampling, bootstrapping, and nested resampling are prominent methods for model validation and ...

 

Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping

  
Remote Sensing of Environment, Vol. 74, No. 3. (December 2000), pp. 545-556, https://doi.org/10.1016/s0034-4257(00)00145-0

Abstract

This article discusses two new methods for increasing the accuracy of classifiers used land cover mapping. The first method, called the product rule, is a simple and general method of combining two or more classification rules as a single rule. Stacked regression methods of combining classification rules are discussed and compared to the product rule. The second method of increasing classifier accuracy is a simple nonparametric classifier that uses spatial information for classification. Two data sets used for land cover mapping ...

 

Bagging ensemble selection for regression

  
In AI 2012: Advances in Artificial Intelligence, Vol. 7691 (2012), pp. 695-706, https://doi.org/10.1007/978-3-642-35101-3_59

Abstract

Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have shown that using random trees as base classifiers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, superior to) other ensemble learning strategies, for instance, the original ES algorithm, stacking with linear regression, random forests or boosting. Motivated by ...

 

Bagging ensemble selection

  
In AI 2011: Advances in Artificial Intelligence, Vol. 7106 (2011), pp. 251-260, https://doi.org/10.1007/978-3-642-25832-9_26

Abstract

Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The method has been highlighted in winning solutions of many data mining competitions, such as the Netflix competition, the KDD Cup 2009 and 2010, the UCSD FICO contest 2010, and a number of data mining competitions on the Kaggle platform. In this paper we present a novel variant: bagging ensemble selection. ...

 

(INRMM-MiD internal record) List of keywords of the INRMM meta-information database - part 10

  
(February 2014)
Keywords: cossus-cossus   cost-benefit-analysis   costal-dunes   costs   cotinus-coggygria   cotoneaster-integerrimus   cotoneaster-nebrodensis   cotoneaster-spp   cotton   couroupita-guianensis   cowania-mexicana   crataegus-azarolus   crataegus-laevigata   crataegus-monogyna   crataegus-nigra   crataegus-spp   creative-commons   crescentia-cujete   crimean-mountains   crisis   croatia   crocidura-suaveolens   cronartium-ribicola   crop-production   crop-yield   crops   cross-disciplinary-perspective   cross-validation   crowd-sourcing   crowdfunding   crowdsourcing   crown-copyright   crown-diameter   crustaceans   cryphalus-piceae   cryphonectria-parasitica   crypmmeria-japonica   cryptomeria-fortunei   cryptomeria-japonica   cryptomeria-spp   cryptorhynchus-lapathi   csmfa   cultivars   cultivated   cultivated-plants   cultural-services   cunninghamia-lanceolata   cupressaceae   cupressus-abramsiana   cupressus-arizonica   cupressus-atlantica   cupressus-bakeri   cupressus-cashmeriana   cupressus-dupreziana   cupressus-funebris   cupressus-goveniana   cupressus-guadalupensis   cupressus-lusitanica   cupressus-macnabiana   cupressus-macrocarpa   cupressus-pygmaea   cupressus-sargentii   cupressus-sempervirens   cupressus-torulosa   curculio-elephas   curculionidae   curiosity   curse-of-dimensionality   curtobacterium-flaccumfaciens   cut-timber   cyanobacteria   cyathea-arborea   cyber-security   cybernetics   cyc   cycadopsida   cyclocarya-paliurus   cyclomatic-complexity   cyclone   cyclostationarity   cydonia-oblonga   cylindrocladium-quinqueseptatum   cyprus   cystopteris-spp   cytisus-scoparius   cytisus-spp   czech-republic   daboecia-cantabrica   dacryodes-excelsa   daktulosphaira-vitifoliae   danae-racemosa   danube-basin   daphne-alboviana   daphne-blagayana   daphne-cneorum   daphne-laureola   daphne-mezereum   daphne-pontica   daphniphyllum-oldhamii   inrmm-list-of-tags  

Abstract

List of indexed keywords within the transdisciplinary set of domains which relate to the Integrated Natural Resources Modelling and Management (INRMM). In particular, the list of keywords maps the semantic tags in the INRMM Meta-information Database (INRMM-MiD). [\n] The INRMM-MiD records providing this list are accessible by the special tag: inrmm-list-of-tags ( http://mfkp.org/INRMM/tag/inrmm-list-of-tags ). ...

This page of the database may be cited as:
Integrated Natural Resources Modelling and Management - Meta-information Database. http://mfkp.org/INRMM/tag/cross-validation

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.