Debt out of control: The links between self-control, compulsive buying, and real debts, In: Journal of Economic Psychology, vol.49, pp.141-149, 2015. ,
The Treatment of Missing Values and its Effect on Classifier Accuracy, Classification, Clustering, and Data Mining Applications, pp.639-647, 1995. ,
A Framework for Clustering Evolving Data Streams, Proceedings 2003 VLDB Conference, pp.81-92, 2003. ,
Database mining: A performance perspective, In: IEEE transactions on knowledge and data engineering, vol.5, pp.914-925, 1993. ,
Bankruptcy prediction for credit risk using neural networks: a survey and new results, IEEE Neural Networks Council, vol.12, pp.929-935, 2001. ,
Human memory: A proposed system and its control processes1, Psychology of learning and motivation, vol.2, pp.89-195, 1968. ,
Working memory, In: Psychology of learning and motivation, vol.8, pp.47-89, 1974. ,
Early Drift Detection Method, 4th ECML PKDD International Workshop on Knowledge Discovery from Data Streams, pp.77-86, 2006. ,
A study of k-nearest neighbour as an imputation method, In: Frontiers in Artificial Intelligence and Applications, vol.87, pp.251-260, 2002. ,
A Study of the Behavior of Several Methods for Balancing Machine Learning Training Data, In: ACM SIGKDD Explorations Newsletter -Special issue on learning from imbalanced datasets, vol.6, pp.20-29, 2004. ,
Optimal and Adaptive Algorithms for Online Boosting, In: Sustainable Energy, Grids and Networks, vol.7, pp.70-79, 2015. ,
Ensembles of Restricted Hoeffding Trees, In: ACM Transactions on Intelligent Systems and Technology, vol.3, issue.2, pp.1-20, 2012. ,
Learning from TimeChanging Data with Adaptive Windowing, Proceedings of the 2007 SIAM International Conference on Data Mining, pp.443-448, 2007. ,
Adaptive Learning from Evolving Data Streams, 8th International Symposium on Intelligent Data Analysis, pp.249-260, 2009. ,
Machine Learning for Data Streams with Practical Examples in MOA, 2018. ,
Moa: Massive online analysis, In: Journal of Machine Learning Research, vol.11, pp.1601-1604, 2010. ,
DATA STREAM MINING A Practical Approach, 2011. ,
In: Joint European conference on machine learning and knowledge discovery in databases, pp.135-150, 2010. ,
Efficient data stream classification via probabilistic adaptive windows, Proceedings of the 28th annual ACM symposium on applied computing, pp.801-806, 2013. ,
Pattern Recognition and Machine Learning, 2006. ,
Classification and Regression Trees, 1984. ,
Pasting Small Votes for Classification in Large Databases and On-Line, In: Machine Learning, vol.36, pp.85-103, 1999. ,
Conditional Likelihood Maximisation: A Unifying Framework for Mutual Information Feature Selection, In: Journal of Machine Learning Research, vol.13, pp.27-66, 2012. ,
Prediction of commercial bank failure via multivariate statistical analysis of financial structures: The Turkish case, European Journal of Operational Research, vol.166, pp.528-546, 2005. ,
An empirical comparison of supervised learning algorithms, Proceedings of the 23rd international conference on Machine learning C, pp.161-168, 2006. ,
SMOTE: Synthetic minority oversampling technique, In: Journal of Artificial Intelligence Research, vol.16, pp.321-357, 2002. ,
Data-intensive applications, challenges, techniques and technologies: A survey on Big Data, In: Information Sciences, vol.275, pp.314-347, 2014. ,
XGBoost: A Scalable Tree Boosting System, Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD '16, pp.785-794, 2016. ,
Household Debt and the European Crisis, European Credit Research Institute, vol.13, 2013. ,
, The over-indebtedness of European households: updated mapping of the situation, nature and causes, effects and initiatives for alleviating its impact, 2013.
Online Passive-Aggressive Algorithms, In: Journal of Machine Learning Research, vol.7, pp.551-585, 2006. ,
Present position and potential developments: Some personal views: Statistical theory: The prequential approach, In: Journal of the Royal Statistical Society. Series A, pp.278-292, 1984. ,
Maximum likelihood from incomplete data via the EM algorithm, In: Journal of the Royal Statistical Society Series B Methodological, vol.39, pp.1-38, 1977. ,
Ensemble Methods in Machine Learning, pp.1-15, 2000. ,
Mining High-speed Data Streams, Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD '00, pp.71-80, 2000. ,
DOI : 10.1145/347090.347107
, , 2012.
Weighted random sampling with a reservoir, In: Information Processing Letters, vol.97, issue.5, pp.181-185, 2006. ,
An introduction to ROC analysis, In: Pattern Recognition Letters, vol.27, pp.861-874, 2006. ,
DOI : 10.1016/j.patrec.2005.10.010
Self-organising map for data imputation and correction in surveys, 2002. ,
Fast Binary Feature Selection with Conditional Mutual Information, In: Journal of Machine Learning Research, vol.5, pp.1531-1555, 2004. ,
Overindebtedness New evidence from the EU-SILC special module, 2010. ,
Racing committees for large datasets, In: Discovery Science, pp.153-164, 2002. ,
DOI : 10.1007/3-540-36182-0_15
URL : https://researchcommons.waikato.ac.nz/bitstream/10289/39/1/content.pdf
Boosting a weak learning algorithm by majority, In: Information and computation, vol.121, pp.256-285, 1995. ,
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting, In: Journal of Computer and System Sciences, pp.119-139, 1997. ,
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2009. ,
The Folk Theorem in Repeated Games with Discounting or with Incomplete Information, In: Econometrica, vol.54, issue.3, p.533, 1986. ,
Recurrent concepts in data streams classification, In: Knowledge and Information Systems, vol.40, pp.489-507, 2014. ,
Learning with Drift Detection, pp.286-295, 2004. ,
DOI : 10.1007/978-3-540-28645-5_29
On evaluating stream learning algorithms, In: Machine Learning, vol.90, pp.317-346, 2013. ,
DOI : 10.1007/s10994-012-5320-9
URL : https://link.springer.com/content/pdf/10.1007%2Fs10994-012-5320-9.pdf
A survey on concept drift adaptation, In: ACM Computing Surveys, vol.46, pp.1-37, 2014. ,
Self-control, financial literacy and consumer over-indebtedness, In: Journal of Economic Psychology, vol.33, pp.590-602, 2012. ,
DOI : 10.1016/j.joep.2011.11.006
URL : https://doi.org/10.1016/j.joep.2011.11.006
Extremely randomized trees, Machine Learning, vol.63, pp.3-42, 2006. ,
DOI : 10.1007/s10994-006-6226-1
URL : https://hal.archives-ouvertes.fr/hal-00341932
Consumer Over-indebtedness in the EU: Measurement and Characteristics, In: Journal of Economic Studies, vol.34, issue.2, 2007. ,
Adaptive random forests for evolving data stream classification, Machine Learning, vol.106, p.15730565, 2017. ,
An Introduction to Variable and Feature Selection, In: Journal of Machine Learning Research (JMLR), vol.3, issue.3, pp.1157-1182, 2003. ,
Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark, In: Pattern Recognition Letters, vol.28, pp.1438-1444, 2007. ,
The WEKA Data Mining Software: An Update, In: SIGKDD Explor. Newsl, vol.11, issue.1, pp.10-18, 2009. ,
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning, Advances in intelligent computing, vol.17, pp.878-887, 2005. ,
Learning from imbalanced data, IEEE Transactions on Knowledge and Data Engineering, vol.21, pp.1263-1284, 2009. ,
Long short-term memory, Neural computation 9, vol.8, pp.1735-1780, 1997. ,
Amelia II: A Program for Missing Data, In: Journal of Statistical Software, vol.45, pp.1-47, 2011. ,
Balancing the balance: Self-control mechanisms and compulsive buying, In: Journal of Economic Psychology, vol.49, pp.120-132, 2015. ,
Credit rating analysis with support vector machines and neural networks: a market comparative study, In: Decision Support Systems, vol.37, pp.543-558, 2004. ,
Mining time-changing data streams, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp.97-106, 2001. ,
Learning model trees from evolving data streams, Data mining and knowledge discovery, vol.23, pp.128-168, 2011. ,
Online Multiclass Boosting, Advances in Neural Information Processing Systems, pp.919-928, 2017. ,
Thinking, fast and slow, 2011. ,
Credit use: Psychological perspectives on a multifaceted phenomenon, pp.1-27, 2012. ,
Locally linear reconstruction based missing value imputation for supervised learning, In: Neurocomputing, vol.118, pp.65-78, 2013. ,
Analyzing Incomplete Political Science Data, In: American Political Science Review, vol.85, pp.49-69, 2001. ,
VHT: Vertical hoeffding tree, IEEE, pp.915-922, 2016. ,
A Data Mining framework to model Consumer Indebtedness with Psychological Factors, IEEE International Conference of Data Mining: The Seventh International Workshop on Domain Driven Data Mining, 2014. ,
Building High-level Features Using Large Scale Unsupervised Learning, Proceedings of the 29th International Coference on International Conference on Machine Learning, pp.507-514, 2012. ,
The Fuzzy C-means Algorithm with Fuzzy P-mode Prototypes for Clustering Objects Having Mixed Features, In: Fuzzy Sets Syst, vol.160, 2009. ,
DOI : 10.1016/j.fss.2009.06.015
Towards Missing Data Imputation: A Study of Fuzzy K-means Clustering Method, Rough Sets and Current Trends in Computing: 4th International Conference, 2004. ,
, , pp.573-579, 2004.
Feature Selection: A Data Perspective, In: Journal of Machine Learning Research, pp.1-73, 2016. ,
DOI : 10.1145/3136625
URL : http://arxiv.org/pdf/1601.07996
Statistical analysis with missing data, 2002. ,
Exploratory under-sampling for class-imbalance learning, Proceedings -IEEE International Conference on Data Mining, ICDM, pp.965-969, 2006. ,
KNN classifier with self adjusting memory for heterogeneous concept drift, Proceedings -IEEE International Conference on Data Mining, ICDM, vol.1, p.15504786, 2017. ,
DOI : 10.1109/icdm.2016.0040
URL : https://pub.uni-bielefeld.de/download/2907622/2907623/Drift.pdf
Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters, In: Theoretical Computer Science, vol.410, pp.1749-1764, 2009. ,
Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters, In: Expert Syst. Appl, vol.28, issue.4, pp.603-614, 2005. ,
Hybrid genetic algorithms and support vector machines for bankruptcy prediction, In: Expert Systems with Applications, vol.31, issue.3, pp.652-660, 2006. ,
DDD: A new ensemble approach for dealing with concept drift, IEEE Transactions on Knowledge and Data Engineering, vol.24, pp.619-633, 2012. ,
DOI : 10.1109/tkde.2011.58
URL : http://www.cs.bham.ac.uk/%7Exin/papers/MinkuYao2011TKDE.pdf
Predicting over-indebtedness on batch and streaming data, 2017 IEEE International Conference on Big Data, pp.1504-1513, 2017. ,
DOI : 10.1109/bigdata.2017.8258084
Learning Fast and Slow: A Unified Batch/Stream Framework, 2018 IEEE International Conference on Big Data, pp.1065-1072, 2018. ,
DOI : 10.1109/bigdata.2018.8622222
Adaptive XGBoost for Evolving Data Streams, 2018. ,
A Hybrid Framework for Scalable Model-based Imputation, p.137, 2018. ,
Scalable Model-Based Cascaded Imputation of Missing Data, Advances in Knowledge Discovery and Data Mining -22nd Pacific-Asia Conference, pp.64-76, 2018. ,
DOI : 10.1007/978-3-319-93040-4_6
Scikit-Multiflow: A Multi-output Streaming Framework, In: Journal of Machine Learning Research, vol.19, pp.1-5, 2018. ,
Imputing Missing Values: The Effect on the Accuracy of Classification, 1998. ,
Deep learning applications and challenges in big data analytics, In: Journal of Big Data, vol.2, issue.1, p.1, 2015. ,
Compulsive buying and life aspirations: An analysis of intrinsic and extrinsic goals, Personality and Individual Differences, vol.76, pp.166-170, 2015. ,
Online Bagging and Boosting, Eighth International Workshop on Artificial Intelligence and Statistics, pp.105-112, 2001. ,
Continuous inspection schemes, pp.100-115 ,
Scikitlearn: Machine learning in Python, In: Journal of machine learning research, vol.12, pp.2825-2830, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-00650905
Ensemble based systems in decision making, IEEE Circuits and Systems Magazine, vol.6, issue.3, pp.21-44, 2006. ,
Learn++: An incremental learning algorithm for supervised neural networks, IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol.31, pp.497-508, 2001. ,
DOI : 10.1109/5326.983933
URL : http://www.cs.iastate.edu/~honavar/Papers/ieeetnnrobi.pdf
{POP} algorithm: Kernel-based imputation to treat missing values in knowledge discovery from databases, In: Expert Systems with Applications, vol.36, pp.957-4174, 2009. ,
DOI : 10.1016/j.eswa.2008.01.059
Nonparametric estimation of regression functions with both categorical and continuous data, In: Journal of Econometrics, vol.119, pp.304-4076, 2004. ,
Missing value imputation using decision trees and decision forests by splitting and merging records: Two novel techniques, In: KnowledgeBased Systems, vol.53, p.9507051, 2013. ,
Missing value imputation using a fuzzy clustering-based EM approach, In: Knowledge and Information Systems, pp.389-422, 2015. ,
Study of Paths Leading to Overindebtedness, Banque de France, 2014. ,
Bankruptcy prediction in banks and firms via statistical and intelligent techniques -A review, European Journal of Operational Research, vol.180, pp.1-28, 2007. ,
Batch-incremental versus instance-incremental learning in dynamic and evolving data, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp.313-323, 2012. ,
DOI : 10.1007/978-3-642-34156-4_29
Classifier Chains for Multi-label Classification, pp.254-269, 2009. ,
DOI : 10.1007/s10994-011-5256-5
URL : http://www.cs.waikato.ac.nz/~eibe/pubs/chains.pdf
MEKA: A Multi-label/Multi-target Extension to Weka, In: Journal of Machine Learning Research, vol.17, 2016. ,
Missing data imputation through machine learning algorithms, In: Artificial Intelligence Methods in the Environmental Sciences, pp.153-169, 2009. ,
Boosting Classifiers for Drifting Concepts, In: Intelligent Data Analysis, vol.11, issue.1, pp.1-40, 2007. ,
DOI : 10.3233/ida-2007-11102
URL : https://eldorado.tu-dortmund.de/bitstream/2003/22236/1/tr06-06.pdf
An application of support vector machines in bankruptcy prediction model, In: Expert Systems with Applications, vol.28, pp.127-135, 2005. ,
A genetic algorithm application in bankruptcy prediction modeling, In: Expert Systems with Applications, vol.23, pp.321-328, 2002. ,
DOI : 10.1016/s0957-4174(02)00051-9
A survey of big data management: Taxonomy and state-of-the-art, In: Journal of Network and Computer Applications, vol.71, pp.151-166, 2016. ,
Indicators of personal financial debt using a multi-disciplinary behavioral model, In: Journal of Economic Psychology, vol.27, issue.4, pp.543-556, 2006. ,
A streaming ensemble algorithm (SEA) for large-scale classification, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp.377-382, 2001. ,
DOI : 10.1145/502512.502568
Using Classifier-Based Nominal Imputation to Improve Machine Learning, Advances in Knowledge Discovery and Data Mining, Pt I: 15th Pacific-Asia Conference, vol.6634, pp.124-135, 2011. ,
DOI : 10.1007/978-3-642-20841-6_11
URL : http://papersdb.cs.ualberta.ca/~papersdb/uploaded_files/1052/paper_2011_pakdd.pdf
Feature selection in bankruptcy prediction, vol.22, pp.120-127, 2009. ,
DOI : 10.1016/j.knosys.2008.08.002
Using Neural Network Ensembles for Bankruptcy Prediction and Credit Scoring, In: Expert Syst. Appl, vol.34, issue.4, pp.2639-2649, 2008. ,
BoostVHT, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management -CIKM '17, pp.899-908, 2017. ,
DOI : 10.1145/3132847.3132974
Random sampling with a reservoir, In: ACM Transactions on Mathematical Software, vol.11, pp.37-57, 1985. ,
DOI : 10.1145/3147.3165
URL : http://www.cs.umd.edu/~samir/498/vitter.pdf
Mining concept-drifting data streams using ensemble classifiers, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining -KDD '03, vol.42, p.226, 2003. ,
DOI : 10.1145/956750.956778
Estimation of missing values using a weighted k-nearest neighbors algorithm, Proceedings -2009 International Conference on Environmental Science and Information Application Technology, pp.660-663, 2009. ,
Data Visualization and Feature Selection: New Algorithms for Nongaussian Data, Advances in Neural Information Processing Systems, vol.12, pp.687-693, 1999. ,
Clustering-based missing value imputation for data preprocessing, In: Industrial Informatics, pp.1081-1086, 2006. ,
DOI : 10.1109/indin.2006.275767
kNN Approach to Unbalanced Data Distributions: A Case Study involving Information Extraction, Workshop on Learning from Imbalanced Datasets II ICML, pp.42-48, 2003. ,
Missing value estimation for mixed-attribute data sets, IEEE Transactions on Knowledge and Data Engineering, vol.23, pp.110-121, 2011. ,
DOI : 10.1109/tkde.2010.99
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