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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2020
عنوان انگلیسی مقاله:
Predicting and explaining corruption across countries: A machine learning approach
ترجمه فارسی عنوان مقاله:
پیش بینی و توضیح فساد در سراسر کشور: رویکرد یادگیری ماشینی
منبع:
Sciencedirect - Elsevier - Government Information Quarterly, 37 (2020) 101407: doi:10:1016/j:giq:2019:101407
نویسنده:
Marcio Salles Melo Limaa, Dursun Delenb,⁎
چکیده انگلیسی:
In the era of Big Data, Analytics, and Data Science, corruption is still ubiquitous and is perceived as one of the
major challenges of modern societies. A large body of academic studies has attempted to identify and explain the
potential causes and consequences of corruption, at varying levels of granularity, mostly through theoretical
lenses by using correlations and regression-based statistical analyses. The present study approaches the phenomenon
from the predictive analytics perspective by employing contemporary machine learning techniques to
discover the most important corruption perception predictors based on enriched/enhanced nonlinear models
with a high level of predictive accuracy. Specifically, within the multiclass classification modeling setting that is
employed herein, the Random Forest (an ensemble-type machine learning algorithm) is found to be the most
accurate prediction/classification model, followed by Support Vector Machines and Artificial Neural Networks.
From the practical standpoint, the enhanced predictive power of machine learning algorithms coupled with a
multi-source database revealed the most relevant corruption-related information, contributing to the related
body of knowledge, generating actionable insights for administrator, scholars, citizens, and politicians. The
variable importance results indicated that government integrity, property rights, judicial effectiveness, and
education index are the most influential factors in defining the corruption level of significance
Keywords: Corruption perception | Machine learning | Predictive modeling | Random forest | Society policies and regulations |Government integrity | Social development
قیمت: رایگان
توضیحات اضافی:
تعداد نظرات : 1
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