Adult Income Data Set
Classify Adult Income
Ronny Kohavi and Barry Becker
Data Mining and Visualization
e-mail: ronnyk '@' live.com for questions.
Data Set Information:
Extraction was done by Barry Becker from the 1994 Census database. A set of reasonably clean records was extracted using the following conditions: ((AAGE>16) && (AGI>100) && (AFNLWGT>1)&& (HRSWK>0))
Prediction task is to determine whether a person makes over 50K a year.
Ron Kohavi, "Scaling Up the Accuracy of Naive-Bayes Classifiers: a Decision-Tree Hybrid", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, 1996
Papers That Cite This Data Set1:
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Rich Caruana and Alexandru Niculescu-Mizil and Geoff Crew and Alex Ksikes. Ensemble selection from libraries of models. ICML. 2004. [View Context].
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Wei-Chun Kao and Kai-Min Chung and Lucas Assun and Chih-Jen Lin. Decomposition Methods for Linear Support Vector Machines. Neural Computation, 16. 2004. [View Context].
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Alexander J. Smola and Vishy Vishwanathan and Eleazar Eskin. Laplace Propagation. NIPS. 2003. [View Context].
I. Yoncaci. Maximum a Posteriori Tree Augmented Naive Bayes Classifiers. O EN INTEL.LIG ` ENCIA ARTIFICIAL CSIC. 2003. [View Context].
Christopher R. Palmer and Christos Faloutsos. Electricity Based External Similarity of Categorical Attributes. PAKDD. 2003. [View Context].
S. Sathiya Keerthi and Chih-Jen Lin. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel. Neural Computation, 15. 2003. [View Context].
Thomas Serafini and G. Zanghirati and Del Zanna and T. Serafini and Gaetano Zanghirati and Luca Zanni. DIPARTIMENTO DI MATEMATICA. Gradient Projection Methods for. 2003. [View Context].
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S. Sathiya Keerthi and Kaibo Duan and Shirish Krishnaj Shevade and Aun Neow Poo. A Fast Dual Algorithm for Kernel Logistic Regression. ICML. 2002. [View Context].
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Jie Cheng and Russell Greiner. Learning Bayesian Belief Network Classifiers: Algorithms and System. Canadian Conference on AI. 2001. [View Context].
Zhiyuan Chen and Johannes Gehrke and Flip Korn. Query Optimization In Compressed Database Systems. SIGMOD Conference. 2001. [View Context].
Stephen D. Bay. Multivariate Discretization for Set Mining. Knowl. Inf. Syst, 3. 2001. [View Context].
Bernhard Pfahringer and Geoffrey Holmes and Richard Kirkby. Optimizing the Induction of Alternating Decision Trees. PAKDD. 2001. [View Context].
Dmitry Pavlov and Jianchang Mao and Byron Dom. Scaling-Up Support Vector Machines Using Boosting Algorithm. ICPR. 2000. [View Context].
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Petri Kontkanen and Jussi Lahtinen and Petri Myllymaki and Tomi Silander and Henry Tirri. Proceedings of Pre- and Post-processing in Machine Learning and Data Mining: Theoretical Aspects and Applications, a workshop within Machine Learning and Applications. Complex Systems Computation Group (CoSCo). 1999. [View Context].
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Ron Kohavi. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. KDD. 1996. [View Context].
Shi Zhong and Weiyu Tang and Taghi M. Khoshgoftaar. Boosted Noise Filters for Identifying Mislabeled Data. Department of Computer Science and Engineering Florida Atlantic University. [View Context].
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Grigorios Tsoumakas and Ioannis P. Vlahavas. Fuzzy Meta-Learning: Preliminary Results. Greek Secretariat for Research and Technology. [View Context].
Josep Roure Alcobe. Incremental Hill-Climbing Search Applied to Bayesian Network Structure Learning. Escola Universitria Politcnica de Mataro. [View Context].
Ayhan Demiriz and Kristin P. Bennett and John Shawe and I. Nouretdinov V.. Linear Programming Boosting via Column Generation. Dept. of Decision Sciences and Eng. Systems, Rensselaer Polytechnic Institute. [View Context].
Chris Giannella and Bassem Sayrafi. An Information Theoretic Histogram for Single Dimensional Selectivity Estimation. Department of Computer Science, Indiana University Bloomington. [View Context].
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Petri Kontkanen and Jussi Lahtinen and Petri Myllymaki and Tomi Silander and Henry Tirri. USING BAYESIAN NETWORKS FOR VISUALIZING HIGH-DIMENSIONAL DATA. Complex Systems Computation Group (CoSCo). [View Context].
Ahmed Hussain Khan and Intensive Care. Multiplier-Free Feedforward Networks. 174. [View Context].
Luc Hoegaerts and J. A. K Suykens and J. Vandewalle and Bart De Moor. Subset Based Least Squares Subspace Regression in RKHS. Katholieke Universiteit Leuven Department of Electrical Engineering, ESAT-SCD-SISTA. [View Context].
David R. Musicant and Alexander Feinberg. Active Set Support Vector Regression. [View Context].
Luc Hoegaerts and J. A. K Suykens and J. Vandewalle and Bart De Moor. Primal Space Sparse Kernel Partial Least Squares Regression for Large Scale Problems Special Session paper . Katholieke Universiteit Leuven Department of Electrical Engineering, ESAT-SCD-SISTA. [View Context].
Kuan-ming Lin and Chih-Jen Lin. A Study on Reduced Support Vector Machines. Department of Computer Science and Information Engineering National Taiwan University. [View Context].
Luca Zanni. An Improved Gradient Projection-based Decomposition Technique for Support Vector Machines. Dipartimento di Matematica, Universitdi Modena e Reggio Emilia. [View Context].
Jeff G. Schneider and Andrew W. Moore. Active Learning in Discrete Input Spaces. School of Computer Science Carnegie Mellon University. [View Context].
Omid Madani and David M. Pennock and Gary William Flake. Co-Validation: Using Model Disagreement to Validate Classification Algorithms. Yahoo! Research Labs. [View Context].
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Please refer to the Machine Learning Repository's citation policy
The evaluation of this dataset is done using Area Under the ROC curve (AUC).
Interpreting the AUROC
Computing the AUROC
Source : http://stats.stackexchange.com/questions/132777/what-does-auc-stand-for-and-what-is-it
- Use of external data is not permitted. This includes use of pre-trained models.
- Hand-labeling is allowed on the training dataset only. Hand-labeling is not permitted on test data and will be grounds for disqualification.