Determination of households benefits from subsidies by using data mining approaches
Bibliographic Data
| ID | 12972005 |
|---|---|
| Authors | S Mahsa Alavi A (0000-0002-8429-4987, Twitter (United States)), Omid Mahdi Ebadati E (0000-0002-2688-9595, Schlumberger (Ireland), corresponding author), Seyed Masoud Alavi Abhari (0000-0003-3092-264X, Twitter (United States)), Towhid Firoozan Sarnaghi (0000-0003-4627-5934, Twitter (United States)) |
| Year | 2022 |
| Volume | 20 |
| Issue | 3 |
| Pages | 303-322 |
| Publication date | 2022-07-20 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Information Technology & Politics (JOURNAL) |
| Journal identifiers | ISSN: 1933-169X • E-ISSN: 1933-1681 |
| Publisher | Routledge (PUBLISHER • GB) |
| DOI | 10.1080/19331681.2022.2097974 |
| OpenAlex | W4285993197 |
| Language | EN |
| Citations received | 1 |
| References cited | 59 |
Poverty, known as a widespread economic and political challenge (specifically at the times of crisis, like COVID-19), is a very complicated problem, which many countries have been trying for a long time to eradicate. Cash-subsidy allocation procedure using traditional statistical vision is the famous approach, which articles have targeted. Inefficiency of these solutions besides the fact that a pair of households with exact same situation will not be existing leads us to inadequacy and inaccuracy of these methods. This study, by putting data mining and machine learning (as well-known majors in IT and computer Science) visions together, draws a path to overcome this challenge. For this aim, the social, income and expenditure dimensions of a dataset are surveyed from 18885 households considered to measure the population poverty ratio (a fuzzy look at on their eligibility). In respect to the different experimental mode, the effective features are being filtered to use in FCM algorithm in order to determine to what extend the households in the poor or wealthy. Moreover, Genetic Algorithm displays its efficiency in the role of optimizer. Finally, the evaluation results show more accurate outcomes from the feature selection technique (on normalized data) and get the optimized clusters
Cash · Data mining · Economic growth · Economics · Inefficiency · Microeconomics · Population · Poverty · Public economics · Sociology · Subsidy · Computer Science · COVID-19 epidemiological studies · COVID-19 Pandemic Impacts · Finance
Introduction to Information Retrieval
A Cluster Separation Measure
Silhouettes
Predicting Poverty Using Geospatial Data in Thailand
Can Wage Subsidies Boost Employment in the Wake of an Economic Crisis? Evidence from Mexico
Counting and multidimensional poverty measurement
Political Agency and Implementation Subsidies with Imperfect Monitoring
The right to stay offline? Not during the pandemic
Poverty Lines Based on Fuzzy Sets Theory and its Application to Malaysian Data
Determining Semi‐Normative Poverty Lines Using Social Survey Data
The politics of poverty and the poverty of politics in Zambia's Third Republic
The impact of innovation and innovation subsidies on economic development in German regions
State subsidies to political parties
On the Estimation of Lower and Upper Bounds of Poverty Line
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |