Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

Neumann, Ursula; Riemenschneider, Mona; Sowa, Jan-Peter; Baars, Theodor; Kälsch, Julia; Canbay, Ali; Heider, Dominik

doi:10.1186/s13040-016-0114-4

Cited by 45 publications

(50 citation statements)

References 37 publications

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“…In order to find features in the output of SEDE-GPS that are predictive for lake microbial biodiversity, we used the R package EFS (Ensemble Feature Selection) and the eight alpha diversity metrics as target variable in separate analyses [13, 14]. EFS is an ensemble feature selection method that assigns weights to the features in an unbiased manner according to their predictiveness for the target value.…”

Section: Resultsmentioning

confidence: 99%

“…Next, we used the R package EFS (Ensemble Feature Selection) in order to rank the remaining features according to their importance. EFS is an ensemble learning feature selection method, that corrects for biases of the single methods when weighting features [13, 14]. Although EFS has been developed for feature selection in classification studies, we used an adapted version of EFS, which can be used for regression studies.…”

Section: Methodsmentioning

confidence: 99%

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SEDE-GPS: socio-economic data enrichment based on GPS information

Sperlea¹,

Füser²,

Boenigk³

et al. 2018

BMC Bioinformatics

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BackgroundMicrobes are essentail components of all ecosystems because they drive many biochemical processes and act as primary producers. In freshwater ecosystems, the biodiversity in and the composition of microbial communities can be used as indicators for environmental quality. Recently, some environmental features have been identified that influence microbial ecosystems. However, the impact of human action on lake microbiomes is not well understood. This is, in part, due to the fact that environmental data is, albeit theoretically accessible, not easily available.ResultsIn this work, we present SEDE-GPS, a tool that gathers data that are relevant to the environment of an user-provided GPS coordinate. To this end, it accesses a list of public and corporate databases and aggregates the information in a single file, which can be used for further analysis. To showcase the use of SEDE-GPS, we enriched a lake microbial ecology sequencing dataset with around 18,000 socio-economic, climate, and geographic features. The sources of SEDE-GPS are public databases such as Eurostat, the Climate Data Center, and OpenStreetMap, as well as corporate sources such as Twitter. Using machine learning and feature selection methods, we were able to identify features in the data provided by SEDE-GPS that can be used to predict lake microbiome alpha diversity.ConclusionThe results presented in this study show that SEDE-GPS is a handy and easy-to-use tool for comprehensive data enrichment for studies of ecology and other processes that are affected by environmental features. Furthermore, we present lists of environmental, socio-economic, and climate features that are predictive for microbial biodiversity in lake ecosystems. These lists indicate that human action has a major impact on lake microbiomes. SEDE-GPS and its source code is available for download at http://SEDE-GPS.heiderlab.deElectronic supplementary materialThe online version of this article (10.1186/s12859-018-2419-4) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

SEDE-GPS: socio-economic data enrichment based on GPS information

Sperlea¹,

Füser²,

Boenigk³

et al. 2018

BMC Bioinformatics

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“…Alternatives to the Boruta method are discussed and evaluated in [22], whereby Boruta was identified as best performing technology among the tested ones if used for different dimensionalities of the data. In 2017 Neumann et al presented the Ensemble Feature Selection (EFS) method which combines multiple FS methods to remove individual biases and give aggregated feature relevance ranges for all of them [23], [24].…”

Section: Introductionmentioning

confidence: 99%

FRI-Feature Relevance Intervals for Interpretable and Interactive Data Exploration

Pfannschmidt

Göpfert

Neumann

et al. 2019

2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB)

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Most existing feature selection methods are insufficient for analytic purposes as soon as high dimensional data or redundant sensor signals are dealt with since features can be selected due to spurious effects or correlations rather than causal effects. To support the finding of causal features in biomedical experiments, we hereby present FRI, an open source Python library that can be used to identify all-relevant variables in linear classification and (ordinal) regression problems. Using the recently proposed feature relevance interval method, FRI is able to provide the base for further general experimentation or in specific can facilitate the search for alternative biomarkers. It can be used in an interactive context, by providing model manipulation and visualization methods, or in a batch process as a filter method.

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“…The following section will briefly introduce our methodology. For deeper insights please refer to [10]. Our EFS currently incorporates eight feature selection methods for binary classifications, namely median, Pearson- and Spearman-correlation, logistic regression, and four variable importance measures embedded in two different implementations of the random forest algorithm, namely cforest [9] and randomForest [13].…”

Section: Methodsmentioning

confidence: 99%

EFS: an ensemble feature selection tool implemented as R-package and web-application

2017

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BackgroundFeature selection methods aim at identifying a subset of features that improve the prediction performance of subsequent classification models and thereby also simplify their interpretability. Preceding studies demonstrated that single feature selection methods can have specific biases, whereas an ensemble feature selection has the advantage to alleviate and compensate for these biases.ResultsThe software EFS (Ensemble Feature Selection) makes use of multiple feature selection methods and combines their normalized outputs to a quantitative ensemble importance. Currently, eight different feature selection methods have been integrated in EFS, which can be used separately or combined in an ensemble.ConclusionEFS identifies relevant features while compensating specific biases of single methods due to an ensemble approach. Thereby, EFS can improve the prediction accuracy and interpretability in subsequent binary classification models.AvailabilityEFS can be downloaded as an R-package from CRAN or used via a web application at http://EFS.heiderlab.de.

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Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach

Cited by 45 publications

References 37 publications

SEDE-GPS: socio-economic data enrichment based on GPS information

SEDE-GPS: socio-economic data enrichment based on GPS information

FRI-Feature Relevance Intervals for Interpretable and Interactive Data Exploration

EFS: an ensemble feature selection tool implemented as R-package and web-application

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