EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 214 (2020) E3S Web Conf., 214 (2020) 01023 References
Open Access
Issue
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
Article Number 01023
Number of page(s) 16
Section Big Data Analysis Application and Energy Consumption Research
DOI https://doi.org/10.1051/e3sconf/202021401023
Published online 07 December 2020
  1. OECD. Long-Term Unemployment. https://stats.oecd.org/glossary/ detail.asp?ID=3586. [Google Scholar]
  2. OECD. A Broken Social Elevator? How to Promote Social Mobility. doi: https://doi.org/10.1787/9789264301085-en (OECD Publishing, Paris, France, 2018). [Google Scholar]
  3. Nichols A. et al. Consequences of Long-Term Unemployment. Urban Institute. https://www.urban.org/sites/default/files/publication/23921/412887-Consequences-of-Long-Term-Unemployment.PDF(Aug. 2013). [Google Scholar]
  4. Payne C. & Payne J. Early Identification of the Long-Term Unemployed. Policy Studies Institute. http://www.psi.org.uk/publications/Research% 20Discussion%20Series/pdffiles/Research DiscussionPaper4.pdf (2000). [Google Scholar]
  5. Regulation (EU) 2016/679 Of The European Parliament And Of The Council (General Data Protection Regulation). Official Journal of the European Union Article 22. https://eurlex.europa.eu/eli/reg/2016/679/oj (Apr. 2016). [Google Scholar]
  6. Goodman B. & Flaxman S. European Union regulations on algorithmic decision-making and a “right to explanation”. AI Magazine 38. https://arxiv.org/pdf/1606.08813.pdf(2017). [Google Scholar]
  7. Silberg J. & Manyika J. Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute (June 2019). [Google Scholar]
  8. Barocas S. & Selbst A. D. Big Data’s Disparate Impact. California Law Review 104. http://dx.doi.org/10.2139/ssrn.2477899(Sept. 2016). [Google Scholar]
  9. Payne C. & Payne J. op. cit. [Google Scholar]
  10. De Rituertode. Troya, ´I. M. et al. Predicting, explaining, and understanding risk of long-term unemployment in 32nd Conference on Neural Information Processing Systems (Montr´eal, Canada, 2018). https://aiforsocialgood.github.io/2018/pdfs/track1/97 aisg neurips 2018.pdf. [Google Scholar]
  11. Denmark: Rosholm et al. , 2006; Ireland: O’Connell, P.J. el al., 2010; Portugal: ´I˜nigo Mart´ınez deRituerto de Troya et al. , 2018 etc. [Google Scholar]
  12. O’Connell, P.J. et al. National Profiling of the Unemployed in Ireland. Research Series RS10. https://ideas.repec.org/b/esr/resser/rs010.html(Economic and Social Research Institute (ESRI), 2009). [Google Scholar]
  13. O’Connell, P.J. et al. A Statistical Profiling Model of Long-Term Unemployment Risk in Ireland. Papers WP345 (Economic and Social Research Institute (ESRI), May 2010). https://ideas.repec.org/p/esr/wpaper/wp345.html. [Google Scholar]
  14. Scikit-learn. StandardScaler. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html [Google Scholar]
  15. Scikit-learn.LogisticRegression.https://scikitlearn.org/stable/modules/generated/sklearn.linearmodel.LogisticRegression.html. [Google Scholar]
  16. Scikit-learn.RandomizedSearchCV.https://scikit-learn.org/stable/modules/generated/sklearn.model selection.Randomized SearchCV.html. [Google Scholar]
  17. Scikit-learn.RandomForestClassifier.https://scikitlearn.org/stable/modules/generated/sklearn.ensemble.RandomForest Classifier.html. [Google Scholar]
  18. Chen T. & Guestrin C. XGBoost: A Scalable Tree Boosting System in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, San Francisco, California, USA, 2016), 785-794.isbn: 978-1-4503-4232-2.http://doi.acm.org/10.1145/2939672.2939785. [Google Scholar]
  19. Chen T. & Guestrin C. XGBoost Parameters.https://xgboost.readthedocs.io/en/latest/parameter.html. [Google Scholar]
  20. Scikit-learn.Model evaluation: quantifying the quality of predictions.https://scikit-learn.org/stable/modules/modelevaluation.html#model-evaluation. [Google Scholar]
  21. Note that the “k” value is ultimately dependent on the PES’s objectives and resources. [Google Scholar]
  22. Lundberg S. & Lee S. A unified approach to interpreting model predictions. CoRR abs/1705.07874. arXiv:1705.07874. http://arxiv.org/abs/1705.07874 (2017). [Google Scholar]
  23. Tseng G. Interpreting complex models with SHAP values. https://medium.com/@gabrieltseng/interpreting-complex-models-with-shap-values-1c187db6ec83(2018). [Google Scholar]
  24. Lundberg, S.M. et al. Consistent Individualized Feature Attribution for Tree Ensembles. ArXiv abs/1802.03888. https://arxiv.org/pdf/1802.03888.pdf (2018). [Google Scholar]
  25. Saleiro P. et al. Aequitas: A Bias and Fairness Audit Toolkit. CoRR abs/1811.05577. arXiv:1811.05577. http://arxiv.org/abs/1811.05577(2018). [Google Scholar]
  26. OECD. A Broken Social Elevator? How to Promote Social Mobility. op. cit. [Google Scholar]
  27. Payne C. & Payne J. op. cit. [Google Scholar]
  28. To clarify, e.g. “Female” is referred to as a group whereas “Sex” is referred to as an attribute. [Google Scholar]
  29. COMPAS Analysis using Aequitas. https://dssg.github.io/aequitas/examples/compassdemo.html(2018). [Google Scholar]
  30. Nichols A. et al. Consequences of Long-Term Unemployment.op.cit. [Google Scholar]
  31. Louie K. Long-Term Unemployment: A Destructive and Persistent Social Issue.www.onlinemswprograms.com/resources/socialissues/long-term-unemployment/. [Google Scholar]
  32. Casselman B. The Biggest Predictor of How Long You’ll Be Unemployed Is When You Lose Your Job.https://fivethirtyeight.com/features/the-biggestpredictor-of-how-long-youll-be-unemployed-iswhen-you-lose-your-job/ (2014). [Google Scholar]
  33. Portugal - Social Integration Income.https://ec.europa.eu/social/main.jsp?catId=1125&langId=en&intPageId=4742. [Google Scholar]
  34. Heckman, J.J. & Borjas, G.J. Does Unemployment Cause Future Unemployment? Definitions, Questions and Answers from a Continuous Time Model of Heterogeneity and State Dependence. Economica 47, 247-283 (Aug. 1980). [Google Scholar]
  35. Louie K. op. cit. [Google Scholar]
  36. Niemi B. The Female-Male Differential in Unemployment Rates. Industrial and Labor Relations Review 27, 331-350. issn: 00197939, 2162271X (1974). [CrossRef] [Google Scholar]
  37. Chamorro-Premuzic T. Will AI Reduce Gender Bias in Hiring? https://hbr.org/2019/06/will-aireducegender-bias-in-hiring(2019). [Google Scholar]
  38. Monge-Naranjo A. & Sohail F. The Composition of Long-term Unemployment Is Changing Toward Older Workers. The Regional Economist. https://www.stlouisfed.org/∼/media/publications/regionaleconomist/2015/october/unemployment.pdf(Oct. 2015). [Google Scholar]
  39. Mitchell J. Who Are the Long-Term Unemployed? Urban Institute. https://www.urban. org/sites/default/files/publication/23911/412885-Who-Are-the-Long-Term- Unemployed-.PDF(Aug. 2013). [Google Scholar]
  40. Jones M. Disability and labor market outcomes. IZA World of Labor. doi: 10.15185/izawol.253 (2016). [Google Scholar]
  41. O’Connell, P.J. et al. National Profiling of the Unemployed in Ireland. op. cit. [Google Scholar]
  42. Chiodo, A.J. & Owyang, M.T. Marriage, Motherhood and Money: How Do Women’s Life Decisions Influence Their Wages? The Regional Economist. https://www.stlouisfed.org/∼/media/files/pdfs/publications/pubassets/pdf/re/2003/b/marriage.pdf(Apr. 2003). [Google Scholar]
  43. De Rituertode. Troya, ´I. M. et al. op. cit. [Google Scholar]
  44. Kraus M. Using Bayesian Optimization to reduce the time spent on hyperparameter tuning. https://medium.com/vantageai/bringing-back-the-time-spent-on-hyperparameter-tuning-with-bayesian-optimisation-2e21a3198afb(2019). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.