scholarly journals A Probabilistic Derivation of LASSO and L12-Norm Feature Selections

2019 ◽  
Vol 33 ◽  
pp. 4586-4593 ◽  
Author(s):  
Di Ming ◽  
Chris Ding ◽  
Feiping Nie
Keyword(s):  
Feature Selection ◽  
Sparse Coding ◽  
Point Of View ◽  
Probabilistic Framework ◽  
Independent Point ◽  
Joint Sparsity ◽  
Selection Approach ◽  
Benchmark Datasets ◽  
Feature Selection Approach

LASSO and ℓ2,1-norm based feature selection had achieved success in many application areas. In this paper, we first derive LASSO and ℓ1,2-norm feature selection from a probabilistic framework, which provides an independent point of view from the usual sparse coding point of view. From here, we further propose a feature selection approach based on the probability-derived ℓ1,2-norm. We point out some inflexibility in the standard feature selection that the feature selected for all different classes are enforced to be exactly the same using the widely used ℓ2,1-norm, which enforces the joint sparsity across all the data instances. Using the probabilityderived ℓ1,2-norm feature selection, allowing certain flexibility that the selected features do not have to be exactly same for all classes, the resulting features lead to better classification on six benchmark datasets.

scholarly journals A Constrained Feature Selection Approach Based on Feature Clustering and Hypothesis Margin Maximization

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Samah Hijazi ◽  
Vinh Truong Hoang
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Subset ◽  
Feature Clustering ◽  
Discriminative Feature ◽  
Selection Approach ◽  
Benchmark Datasets ◽  
Margin Maximization ◽  
Feature Selection Approach ◽  
The Right

In this paper, we propose a semisupervised feature selection approach that is based on feature clustering and hypothesis margin maximization. The aim is to improve the classification accuracy by choosing the right feature subset and to allow building more interpretable models. Our approach handles the two core aspects of feature selection, i.e., relevance and redundancy, and is divided into three steps. First, the similarity weights between features are represented by a sparse graph where each feature can be reconstructed from the sparse linear combination of the others. Second, features are then hierarchically clustered identifying groups of the most similar ones. Finally, a semisupervised margin-based objective function is optimized to select the most data discriminative feature from within each cluster, hence maximizing relevance while minimizing redundancy among features. Eventually, we empirically validate our proposed approach on multiple well-known UCI benchmark datasets in terms of classification accuracy and representation entropy, where it proved to outperform four other semisupervised and unsupervised methods and competed with two widely used supervised ones.

scholarly journals An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals

2021 ◽  
Vol 11 (15) ◽  
pp. 6983
Author(s):  
Maritza Mera-Gaona ◽  
Diego M. López ◽  
Rubiel Vargas-Canas
Keyword(s):  
Feature Selection ◽  
Sensitivity And Specificity ◽  
Eeg Signals ◽  
Specificity And Sensitivity ◽  
Selection Approach ◽  
Feature Selection Approach ◽  
The Stability ◽  
Selection Algorithms ◽  
Epileptiform Events

Identifying relevant data to support the automatic analysis of electroencephalograms (EEG) has become a challenge. Although there are many proposals to support the diagnosis of neurological pathologies, the current challenge is to improve the reliability of the tools to classify or detect abnormalities. In this study, we used an ensemble feature selection approach to integrate the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals. Discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and Support Vecctor Machine (SVM); furthermore, performance was assessed by accuracy, specificity, and sensitivity metrics. The evaluation results showed that Ensemble Feature Selection (EFS) is a helpful tool to select relevant features from the EEGs. Thus, the stability calculated for the EFS method proposed was almost perfect in most of the cases evaluated. Moreover, the assessed classifiers evidenced that the models improved in performance when trained with the EFS approach’s features. In addition, the classifier of epileptiform events built using the features selected by the EFS method achieved an accuracy, sensitivity, and specificity of 97.64%, 96.78%, and 97.95%, respectively; finally, the stability of the EFS method evidenced a reliable subset of relevant features. Moreover, the accuracy, sensitivity, and specificity of the EEG detector are equal to or greater than the values reported in the literature.

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