scholarly journals Risk Factor Identification of Sustainable Guarantee Network Based on Logistic Regression Algorithm

2019 ◽  
Vol 11 (13) ◽  
pp. 3525 ◽  
Author(s):  
Han He ◽  
Sicheng Li ◽  
Lin Hu ◽  
Nelson Duarte ◽  
Otilia Manta ◽  
...  
Keyword(s):  
Economic Development ◽  
Logistic Regression ◽  
Prediction Accuracy ◽  
Predictive Accuracy ◽  
Training Model ◽  
Eastern Region ◽  
Close Link ◽  
Factors Influencing ◽  
Logistic Regression Algorithm ◽  
Network Risk

In order to investigate the factors influencing the sustainable guarantee network and its differences in different spatial and temporal scales, logistic regression algorithm is used to analyze the data of listed companies in 31 provinces, municipalities and autonomous regions in China from 2008 to 2017 (excluding Hong Kong, Macau and Taiwan). The study finds that, overall, companies with better profitability, poor solvency, poor operational capability and higher levels of economic development are more likely to join the guarantee network. On the temporal scale, solvency and regional economic development exert increasing higher impact on the companies’ accession to the guarantee network, and operational capacity has increasingly smaller impact. On the spatial scale, the less close link between company executives and companies in the western region suggests higher possibility to join the guarantee network. The predictive accuracy test results of the logistic regression algorithm show that the training model of the western sample enterprises has the highest prediction accuracy when predicting enterprise behavior of joining the guarantee network, while the accuracy is the lowest in the central region. When forecasting enterprises’ failure to join the guarantee network, the training model of the central sample enterprise has the highest accuracy, while the accuracy is the lowest in the eastern region. This paper discusses the internal and external factors influencing the guarantee network risk from the perspective of spatial and temporal differences of the guarantee network, and discriminates the prediction accuracy of the training model, which means certain guiding significance for listed company management, bank and government to identify and control the guarantee network risk.

scholarly journals Enabling Equal Opportunity in Logistic Regression Algorithm

2021 ◽  
Author(s):  
Sandro Radovanović ◽  
Marko Ivić
Keyword(s):  
Logistic Regression ◽  
Prediction Model ◽  
Predictive Model ◽  
Prediction Accuracy ◽  
Equal Opportunity ◽  
Census Data ◽  
Research Question ◽  
Classification Model ◽  
Disparate Impact ◽  
Logistic Regression Algorithm

Research Question: This paper aims at adjusting the logistic regression algorithm to mitigate unwanted discrimination shown towards race, gender, etc. Motivation: Decades of research in the field of algorithm design have been dedicated to making a better prediction model. Many algorithms are designed and improved, which made them better than the judgments of people and even experts. However, in recent years it has been discovered that predictive models can make unwanted discrimination. Such unwanted discrimination in the predictive model can lead to legal consequences. In order to mitigate the problem of unwanted discrimination, we propose equal opportunity between privileged and discriminated groups in the logistic regression algorithm. Idea: Our idea is to add a regularization term in the goal function of the logistic regression. Therefore, our predictive model will solve both the social problem and the predictive problem. More specifically, our model will provide fair and accurate predictions. Data: The data used in this research present U.S. census data describing individuals using personal characteristics with a goal to provide a binary classification model for predicting if an individual has an annual salary above $50k. The dataset used is known for disparate impact regarding female individuals. In addition, we used the COMPAS dataset aimed at predicting recidivism. COMPAS is biased toward African-Americans. Tools: We developed a novel regularization technique for equal opportunity in the logistic regression algorithm. The proposed regularization is compared against classical logistic regression and fairness constraint logistic regression, using a ten-fold cross-validation. Findings: The results suggest that equal opportunity logistic regression manages to create a fair prediction model. More specifically, our model improved both disparate impact and equal opportunity compared to classical logistic regression, with a minor loss in prediction accuracy. Compared to the disparate impact constrained logistic regression, our approach has higher prediction accuracy and equal opportunity, while having a lower disparate impact. By inspecting the coefficients of our approach and classical logistic regression, one can see that proxy attribute coefficients are reduced to very low values. Contribution: The main contribution of this paper is in the methodological part. More specifically, we implemented an equal opportunity in the logistic regression algorithm.

scholarly journals Mental Health Monitoring Based on Multiperception Intelligent Wearable Devices

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xichao Dai ◽  
Yumei Ding
Keyword(s):  
Logistic Regression ◽  
Prediction Accuracy ◽  
Recall Rate ◽  
Wearable Devices ◽  
Decision Fusion ◽  
Statistical Characteristics ◽  
Fusion Model ◽  
Training Samples ◽  
Boosting Method ◽  
Logistic Regression Algorithm

In order to improve the accuracy of the evaluation results of multiperception intelligent wearable devices, the mathematical statistical characteristics based on speech, behavior, environment, and physical signs are proposed; first, the PCA feature compression algorithm was used to reduce the dimension of these features, and the differences among different training samples were compared and analyzed; then, three weak classifiers are designed using the logistic regression algorithm, and finally, a strong classifier with higher prediction accuracy is designed according to the boosting decision fusion method and ensemble learning idea. The results showed that the accuracy of the logistic regression model trained with the feature data of voice PCA was 0.964, but the recall rate and crossover results were significantly reduced to 0.844 and 0.846, respectively. The accuracy, accuracy and recall of the decision fusion model based on the boosting method and integrated learning are 0.969, and the prediction accuracy of K-folds cross-validation is also as high as 0.956; the superposition fusion results of three weak classifiers achieve a better classification effect.

scholarly journals Predicting Egyptian Banks Distress

2018 ◽  
Vol 8 (3) ◽  
pp. 39 ◽  
Author(s):  
Osama EL-Ansary ◽  
Mohamed Saleh
Keyword(s):  
Logistic Regression ◽  
Discriminant Analysis ◽  
Regression Model ◽  
Data Base ◽  
Financial Distress ◽  
Prediction Accuracy ◽  
Logistic Regression Model ◽  
Predictive Accuracy ◽  
Prediction Method ◽  
Accurate Prediction

Purpose – the main purpose of the study is to investigate an accurate prediction method for banking distress applied on a set of Egyptian banks.Methodology - the researchers have compared the prediction accuracy of the discriminant analysis and logistic regression model, to choose the most appropriate one. The data has been collected from the “Bank scope” data base and for the period of 2002–2016.Findings – the results of the study revealed that the predictive accuracy of discriminant analysis outperformed that of the logistic regression model.Originality - The study adds value to the literature as it is one of the few studies that is concerned with predicating the banking financial distress especially in Egypt.

scholarly journals Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
Author(s):  
Hamza Aldabbas ◽  
Abdullah Bajahzar ◽  
Meshrif Alruily ◽  
Ali Adil Qureshi ◽  
Rana M. Amir Latif ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Language Processing ◽  
Mobile Application ◽  
Knowledge Engineering ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
User Reviews ◽  
N Gram ◽  
Logistic Regression Algorithm ◽  
Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

scholarly journals Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4–7th Korea National Health and Nutrition Examination Survey

2021 ◽  
Vol 18 (11) ◽  
pp. 5597
Author(s):  
Hyerim Kim ◽  
Dong Hoon Lim ◽  
Yoona Kim
Keyword(s):  
Diabetes Mellitus ◽  
Logistic Regression ◽  
Decision Tree ◽  
Prediction Accuracy ◽  
Nutritional Intake ◽  
Nutrition Examination Survey ◽  
Learning Models ◽  
Health And Nutrition ◽  
Machine Learning Models

Few studies have been conducted to classify and predict the influence of nutritional intake on overweight/obesity, dyslipidemia, hypertension and type 2 diabetes mellitus (T2DM) based on deep learning such as deep neural network (DNN). The present study aims to classify and predict associations between nutritional intake and risk of overweight/obesity, dyslipidemia, hypertension and T2DM by developing a DNN model, and to compare a DNN model with the most popular machine learning models such as logistic regression and decision tree. Subjects aged from 40 to 69 years in the 4–7th (from 2007 through 2018) Korea National Health and Nutrition Examination Survey (KNHANES) were included. Diagnostic criteria of dyslipidemia (n = 10,731), hypertension (n = 10,991), T2DM (n = 3889) and overweight/obesity (n = 10,980) were set as dependent variables. Nutritional intakes were set as independent variables. A DNN model comprising one input layer with 7 nodes, three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer and one output layer with one node were implemented in Python programming language using Keras with tensorflow backend. In DNN, binary cross-entropy loss function for binary classification was used with Adam optimizer. For avoiding overfitting, dropout was applied to each hidden layer. Structural equation modelling (SEM) was also performed to simultaneously estimate multivariate causal association between nutritional intake and overweight/obesity, dyslipidemia, hypertension and T2DM. The DNN model showed the higher prediction accuracy with 0.58654 for dyslipidemia, 0.79958 for hypertension, 0.80896 for T2DM and 0.62496 for overweight/obesity compared with two other machine leaning models with five-folds cross-validation. Prediction accuracy for dyslipidemia, hypertension, T2DM and overweight/obesity were 0.58448, 0.79929, 0.80818 and 0.62486, respectively, when analyzed by a logistic regression, also were 0.52148, 0.66773, 0.71587 and 0.54026, respectively, when analyzed by a decision tree. This study observed a DNN model with three hidden layers with 30 nodes, 12 nodes, 8 nodes in each layer had better prediction accuracy than two conventional machine learning models of a logistic regression and decision tree.

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

2021 ◽  
pp. 1-10
Author(s):  
I. Krug ◽  
J. Linardon ◽  
C. Greenwood ◽  
G. Youssef ◽  
J. Treasure ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Individual Risk ◽  
European Project ◽  
Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

ASSESSMENT OF THE ACTIVATOR OF SOCIO-ECONOMIC DEVELOPMENT OF THE REGION

2021 ◽  
Vol 3 (12) ◽  
pp. 27-34
Author(s):  
Liana E. Kabisova ◽  
◽  
Noemi A. Mardeyan ◽  
Zarina E. Tarkhanova ◽  
Batraz E. Bagaev ◽  
...  
Keyword(s):  
Economic Development ◽  
Regional Level ◽  
Economic Situation ◽  
Direction Vector ◽  
Key Factors ◽  
Factors Influencing ◽  
Socio Economic Development ◽  
The Future ◽  
Effectiveness And Efficiency

The article identifies the key factors influencing the activity of the socio-economic development of the region. The dynamics of this factor is analyzed. The indicator is investigated for the sufficiency and validity of the application, as well as for the effectiveness and efficiency with the designation of the direction vector, forecast, assessment for the future use of this leverage, to improve the economic situation at the regional level.

The Russian Far East: from a depressed region to the territory of the future

2021 ◽  
Author(s):  
Ol'ga Nikolaychuk
Keyword(s):  
Economic Development ◽  
Russian Far East ◽  
Far East ◽  
Environmental Problems ◽  
Eastern Region ◽  
The Far East ◽  
The Future ◽  
Bilateral Cooperation ◽  
Far Eastern ◽  
The Russian Far East

The monograph presents the search for solutions to the problems of the Far Eastern region. The proximity of China and the remoteness from the center of Russia make us look for effective measures to overcome the problems of settling the Far East in the context of sustainable economic development of modern Russia. The paper analyzes the problems of the Far East: in industry, agriculture, forestry, energy problems, environmental problems, and provides recommendations for their solution. Considerable attention is paid to migration problems. The experience of China is studied through the prism of bilateral cooperation with Russia. It is intended for students, masters, postgraduates, researchers dealing with issues of macroeconomic regulation and forecasting.

The alternative negative constructions in spoken and written Korean: Logistic regression analysis

2019 ◽  
Vol 15 (2) ◽  
pp. 419-442
Author(s):  
Beom-mo Kang
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Mixed Effects ◽  
Written Language ◽  
Regression Analyses ◽  
Relevant Factor ◽  
Factors Influencing ◽  
Frequency Of Use ◽  
Relevant Factors

AbstractAdopting quantitative corpus-based methods, this paper focuses on the alternative negative constructions in Korean, [anV] and [Vanhda]. Logistic regression analyses for a mixed-effects model were carried out on data drawn from the Sejong Korean Corpus. Certain features of the verb or adjective in negative constructions significantly affect the use of the two negative constructions. A relevant factor is register/medium (spoken or written), among other significant interactions of factors. Furthermore, the fact that frequency is consistent with other relevant factors, together with certain diachronic facts of Korean, supports the claim that frequency of use plays an important role in linguistic changes. Another finding is that, notwithstanding noticeable differences between spoken and written language, the factors influencing the use of the two negative constructions in Korean are largely similar in the spoken and written registers.

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