Identifying the most Informative Variables for Decision-Making Problems - a Survey of Recent Approaches and Accompanying Problems

Pudil, Pavel; Somol, Petr

doi:10.18267/j.aop.131

Cited by 12 publications

(9 citation statements)

References 35 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This procedure will be repeated until one of the stopping criteria is achieved. The selection approach belongs to the sequential backward selection methods (Pudil and Somo, 2008). In this study the following stopping criteria are chosen (see Fig.…”

Section: Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of two feature selection methods for the separability analysis of intertidal sediments with spectrometric datasets in the German Wadden Sea

Jung

Ehlers

2016

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

Section: Feature Selectionmentioning

confidence: 99%

“…It was chosen because it is a popular state-of-the-art approach, which is part of many comparative studies of feature selection methods (Pudil and Somo, 2008;Wu et al, 2013;Datta et al, 2014). The following paragraph is a summary of the study of Robnik-Sikonja and Kononenko (2003), which describes the 'ReliefF' algorithm in detail.…”

Section: Relieffmentioning

confidence: 99%

Comparison of two feature selection methods for the separability analysis of intertidal sediments with spectrometric datasets in the German Wadden Sea

Jung

Ehlers

2016

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

“…On the contrary, wrapper techniques depend on a classification algorithm which is used to evaluate the candidate solutions (subsets of features) generated by a search algorithm, and thus are more computationally expensive. In spite of this drawback, they often provide better results, and should be applied whenever possible [19], although some care should be taken to prevent over-fitting, since the classifier used within the wrapper procedure evaluates solutions according to their performance for the training data. Lastly, embedded methods are applied during the classifier learning process to remove features based on the prediction errors of training data [20,21].…”

Section: Introductionmentioning

confidence: 99%

A new multi-objective wrapper method for feature selection – Accuracy and stability analysis for BCI

et al. 2019

View full text Add to dashboard Cite

Feature selection is an important step in building classifiers for high-dimensional data problems, such as EEG classification for BCI applications. This paper proposes a new wrapper method for feature selection, based on a multi-objective evolutionary algorithm, where the representation of the individuals or potential solutions, along with the breeding operators and objective functions, have been carefully designed to select a small subset of features that has good generalization capability, trying to avoid the over-fitting problems that wrapper methods usually suffer. A novel feature ranking procedure is also proposed in order to analyze the stability of the proposed wrapper method. Four different classification schemes have been applied within the proposed wrapper method in order to evaluate its accuracy and stability for feature selection on a real motor imagery dataset. Experimental results show that the wrapper method presented in this paper is able to obtain very small subsets of features, which are quite stable and also achieve high classification accuracy, regardless of the classifiers used.

show abstract

“…Among the three main approaches for feature selection: filter, wrapper, and embedded methods [ 3 ], our proposals in this paper lie inside the wrapper alternative as wrapper and embedded methods are usually recognized as the preferable approaches whenever they would be feasible [ 4 ]. Nevertheless, as the size of the search space depends exponentially on the number of possible features, an exhaustive search for the best feature set is almost impossible when the feature dimension is high.…”

Section: Introductionmentioning

confidence: 99%

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection

Ortega

Asensio-Cubero²,

Gan

et al. 2016

BioMed Eng OnLine

View full text Add to dashboard Cite

BackgroundBrain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI.MethodsThis paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection.Results and conclusionThe statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal–Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed.

show abstract

Identifying the most Informative Variables for Decision-Making Problems - a Survey of Recent Approaches and Accompanying Problems

Cited by 12 publications

References 35 publications

Comparison of two feature selection methods for the separability analysis of intertidal sediments with spectrometric datasets in the German Wadden Sea

Comparison of two feature selection methods for the separability analysis of intertidal sediments with spectrometric datasets in the German Wadden Sea

A new multi-objective wrapper method for feature selection – Accuracy and stability analysis for BCI

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection

Contact Info

Product

Resources

About