Feature selection and classification of speech under long-term stress

2015 ◽  
Author(s):  
Bin Hu ◽  
Zhenyu Liu ◽  
Lihua Yan ◽  
Tianyang Wang ◽  
Fei Liu ◽  
...  
Keyword(s):  
Feature Selection

scholarly journals Feature Selection for Interpatient Supervised Heart Beat Classification

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
G. Doquire ◽  
G. de Lannoy ◽  
D. François ◽  
M. Verleysen
Keyword(s):  
Feature Selection ◽  
Domain Knowledge ◽  
Heart Beat ◽  
Classification Models ◽  
Feature Sets ◽  
Beat Classification ◽  
Feature Selection Techniques ◽  
Optimal Feature

Supervised and interpatient classification of heart beats is primordial in many applications requiring long-term monitoring of the cardiac function. Several classification models able to cope with the strong class unbalance and a large variety of feature sets have been proposed for this task. In practice, over 200 features are often considered, and the features retained in the final model are either chosen using domain knowledge or an exhaustive search in the feature sets without evaluating the relevance of each individual feature included in the classifier. As a consequence, the results obtained by these models can be suboptimal and difficult to interpret. In this work, feature selection techniques are considered to extract optimal feature subsets for state-of-the-art ECG classification models. The performances are evaluated on real ambulatory recordings and compared to previously reported feature choices using the same models. Results indicate that a small number of individual features actually serve the classification and that better performances can be achieved by removing useless features.

MODERN PROBLEMATICS AND ANALYSIS OF EFFICIENCY OF APPLICATION OF DIFFERENT APPROACHES TO DESTRUCTION OF INFORMATION ON MAGNETIC MEDIA

2019 ◽  
Vol 2 (12) ◽  
pp. 154-164
Author(s):  
O. Semenenko ◽  
O. Vodchyts ◽  
V. Koverga ◽  
R. Lukash ◽  
O. Lutsenko
Keyword(s):  
Armed Forces ◽  
Information Storage ◽  
Storage Device ◽  
Magnetic Media ◽  
Long Term Storage ◽  
Physical Impact ◽  
And Storage ◽  
Term Storage

The introduction and active use of information transmission and storage systems in the Ministry of Defense (MoD) of Ukraine form the need to develop ways of guaranteed removal of data from media after their use or long-term storage. Such a task is an essential component of the functioning of any information security system. The article analyzes the problems of guaranteed destruction of information on magnetic media. An overview of approaches to the guaranteed destruction of information on magnetic media of different types is presented, and partial estimates of the effectiveness of their application are given by some generally accepted indicators of performance evaluation. The article also describes the classification of methods of destruction of information depending on the influence on its medium. The results of the analysis revealed the main problems of application of software methods and methods of demagnetization of the information carrier. The issue of guaranteed destruction of information from modern SSD devices, which are actively used in the formation of new systems of information accumulation and processing, became particularly relevant in the article. In today's conditions of development of the Armed Forces of Ukraine, methods of mechanical and thermal destruction are more commonly used today. In the medium term, the vector of the use of information elimination methods will change towards the methods of physical impact by the pulsed magnetic field and the software methods that allow to store the information storage device, but this today requires specialists to develop new ways of protecting information in order to avoid its leakage.

Influence of internal and external risks on sustainability of financial system of Аltai krai

2018 ◽  
Vol 35 (4) ◽  
pp. 133-136
Author(s):  
R. N. Ibragimov
Keyword(s):  
Sustainable Development ◽  
Financial Stability ◽  
Management Strategy ◽  
Financial System ◽  
Risk Management Strategy ◽  
The Sustainable Development ◽  
The Stability ◽  
The Impact

The article examines the impact of internal and external risks on the stability of the financial system of the Altai Territory. Classification of internal and external risks of decline, affecting the sustainable development of the financial system, is presented. A risk management strategy is proposed that will allow monitoring of risks, thereby these measures will help reduce the loss of financial stability and ensure the long-term development of the economy of the region.

A neural computing-based cash price prediction using multi-layer perceptron (MLP) and feature selection for health economics

2019 ◽  
Vol 14 ◽  
Author(s):  
Zinat Ansari
Keyword(s):  
Feature Selection ◽  
Health Economics ◽  
Cash Flow ◽  
Multi Layer Perceptron ◽  
Asset Sales ◽  
Fixed Asset ◽  
Academic Fields ◽  
Investment Returns ◽  
Short And Long Term

Background: The present study proceeds to incorporate feature selection as a means for selecting the most relevant features affecting the prediction of cash prices in Iran in terms of health economics. Health economics are between academic fields that can aid in ameliorating conditions so as to perform better decisions in regards to the economy such as determining cash prices. Methods: Accordingly, a series of search algorithms, namely the Best-First, Greedy-Stepwise, and Ranker methods, are deployed in order to extract the most relevant features from among a 500 data samples. The validity of the methods was evaluated via the LMT procedure. The corresponding dataset used for this study constitutes a variety of features including net cash flow, dividends, revenue from short and long-term deposits, cash flow from investment returns, income tax, fixed asset purchases, fixed asset sales, long-term investment purchases, long-term investment sales, total cash flow from investment activities, financial facilities, and repayment of financial facilities. Results: The results were indicative of the superiority of the Ranker model using the RelieF-Attribute-Eval tool in Weka over the remaining classification methods. Ergo, the LMT approach could be employed to remove data redundancies and thereby accelerate the estimation process, while saving time and money. The results of the multi-layer perceptron (MLP) further confirmed the high accuracy of the proposed method in estimating cash prices. Conclusions: The present research attempted to reduce the volume of data required for predicting end cash by means of employing a feature selection so as to save both precious money and time.

Using class-based feature selection for the classification of hyperspectral data

2011 ◽  
Vol 32 (15) ◽  
pp. 4311-4326 ◽  
Author(s):  
Yasser Maghsoudi ◽  
Mohammad Javad Valadan Zoej ◽  
Michael Collins
Keyword(s):  
Feature Selection ◽  
Hyperspectral Data ◽  
Selection For

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.

scholarly journals Towards a hydrological classification of European soils: preliminary test of its predictive power for the base flow index using river discharge data

2007 ◽  
Vol 11 (4) ◽  
pp. 1501-1513 ◽  
Author(s):  
M. K. Schneider ◽  
F. Brunner ◽  
J. M. Hollis ◽  
C. Stamm
Keyword(s):  
Water Movement ◽  
Base Flow ◽  
Regression Coefficients ◽  
Northern Europe ◽  
Flow Index ◽  
Discharge Data ◽  
Base Flow Index ◽  
The Uk

Abstract. Predicting discharge in ungauged catchments or contaminant movement through soil requires knowledge of the distribution and spatial heterogeneity of hydrological soil properties. Because hydrological soil information is not available at a European scale, we reclassified the Soil Geographical Database of Europe (SGDBE) at 1:1 million in a hydrological manner by adopting the Hydrology Of Soil Types (HOST) system developed in the UK. The HOST classification describes dominant pathways of water movement through soil and was related to the base flow index (BFI) of a catchment (the long-term proportion of base flow on total stream flow). In the original UK study, a linear regression of the coverage of HOST classes in a catchment explained 79% of BFI variability. We found that a hydrological soil classification can be built based on the information present in the SGDBE. The reclassified SGDBE and the regression coefficients from the original UK study were used to predict BFIs for 103 catchments spread throughout Europe. The predicted BFI explained around 65% of the variability in measured BFI in catchments in Northern Europe, but the explained variance decreased from North to South. We therefore estimated new regression coefficients from the European discharge data and found that these were qualitatively similar to the original estimates from the UK. This suggests little variation across Europe in the hydrological effect of particular HOST classes, but decreasing influence of soil on BFI towards Southern Europe. Our preliminary study showed that pedological information is useful for characterising soil hydrology within Europe and the long-term discharge regime of catchments in Northern Europe. Based on these results, we draft a roadmap for a refined hydrological classification of European soils.

Combined Genotypes of CCR5, CCR2, SDF1, and HLA Genes Can Predict the Long-Term Nonprogressor Status in Human Immunodeficiency Virus-1–Infected Individuals

Blood ◽  
1999 ◽  
Vol 93 (3) ◽  
pp. 936-941 ◽  
Author(s):  
Magdalena Magierowska ◽  
Ioannis Theodorou ◽  
Patrice Debré ◽  
Françoise Sanson ◽  
Brigitte Autran ◽  
...  
Keyword(s):  
Human Immunodeficiency Virus ◽  
Chemokine Receptor ◽  
Multivariate Logistic Regression Model ◽  
Hla B27 ◽  
Hla Alleles ◽  
Immunodeficiency Virus ◽  
Combined Genotypes ◽  
Hiv 1

Abstract Human immunodeficiency virus (HIV)-1–infected long-term nonprogressors (LT-NP) represent less than 5% of HIV-1–infected patients. In this work, we tried to understand whether combined genotypes of CCR5-▵32, CCR2-64I, SDF1-3′A and HLA alleles can predict the LT-NP status. Among the chemokine receptor genotypes, only the frequency of the CCR5-▵32 allele was significantly higher in LT-NP compared with the group of standard progressors. The predominant HLA alleles in LT-NP were HLA-A3, HLA-B14, HLA-B17, HLA-B27, HLA-DR6, and HLA-DR7. A combination of both HLA and chemokine receptor genotypes integrated in a multivariate logistic regression model showed that if a subject is heterozygous for CCR5-▵32 and homozygous for SDF1 wild type, his odds of being LT-NP are increased by 16-fold, by 47-fold when a HLA-B27 allele is present with HLA-DR6 absent, and by 47-fold also if at least three of the following alleles are present: HLA-A3, HLA-B14, HLA-B17, HLA-DR7. This model allowed a correct classification of 70% of LT-NPs and 81% of progressors, suggesting that the host’s genetic background plays an important role in the evolution of HIV-1. The chemokine receptor and chemokine genes along with the HLA genotype can serve as predictors of HIV-1 outcome for classification of HIV-1–infected subjects as LT-NPs or progressors.

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