Adult Data Set
Download: Data Folder, Data Set Description
Abstract: Predict whether income exceeds $50K/yr based on census data. Also known as "Census Income" dataset.
Data Set Characteristics:
Number of Instances:
Number of Attributes:
Number of Web Hits:
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.
Listing of attributes:
workclass: Private, Self-emp-not-inc, Self-emp-inc, Federal-gov, Local-gov, State-gov, Without-pay, Never-worked.
education: Bachelors, Some-college, 11th, HS-grad, Prof-school, Assoc-acdm, Assoc-voc, 9th, 7th-8th, 12th, Masters, 1st-4th, 10th, Doctorate, 5th-6th, Preschool.
marital-status: Married-civ-spouse, Divorced, Never-married, Separated, Widowed, Married-spouse-absent, Married-AF-spouse.
occupation: Tech-support, Craft-repair, Other-service, Sales, Exec-managerial, Prof-specialty, Handlers-cleaners, Machine-op-inspct, Adm-clerical, Farming-fishing, Transport-moving, Priv-house-serv, Protective-serv, Armed-Forces.
relationship: Wife, Own-child, Husband, Not-in-family, Other-relative, Unmarried.
race: White, Asian-Pac-Islander, Amer-Indian-Eskimo, Other, Black.
sex: Female, Male.
native-country: United-States, Cambodia, England, Puerto-Rico, Canada, Germany, Outlying-US(Guam-USVI-etc), India, Japan, Greece, South, China, Cuba, Iran, Honduras, Philippines, Italy, Poland, Jamaica, Vietnam, Mexico, Portugal, Ireland, France, Dominican-Republic, Laos, Ecuador, Taiwan, Haiti, Columbia, Hungary, Guatemala, Nicaragua, Scotland, Thailand, Yugoslavia, El-Salvador, Trinadad&Tobago, Peru, Hong, Holand-Netherlands.
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:
Rakesh Agrawal and Ramakrishnan ikant and Dilys Thomas. Privacy Preserving OLAP. SIGMOD Conference. 2005. [View Context].
Rich Caruana and Alexandru Niculescu-Mizil. An Empirical Evaluation of Supervised Learning for ROC Area. ROCAI. 2004. [View Context].
Rich Caruana and Alexandru Niculescu-Mizil and Geoff Crew and Alex Ksikes. Ensemble selection from libraries of models. ICML. 2004. [View Context].
Bianca Zadrozny. Learning and evaluating classifiers under sample selection bias. ICML. 2004. [View Context].
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].
Saharon Rosset. Model selection via the AUC. ICML. 2004. [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].
Bart Hamers and J. A. K Suykens. Coupled Transductive Ensemble Learning of Kernel Models. Bart De Moor. 2003. [View Context].
Andrew W. Moore and Weng-Keen Wong. Optimal Reinsertion: A New Search Operator for Accelerated and More Accurate Bayesian Network Structure Learning. ICML. 2003. [View Context].
Alexander J. Smola and Vishy Vishwanathan and Eleazar Eskin. Laplace Propagation. NIPS. 2003. [View Context].
Bianca Zadrozny and Charles Elkan. Transforming classifier scores into accurate multiclass probability estimates. KDD. 2002. [View Context].
Nitesh V. Chawla and Kevin W. Bowyer and Lawrence O. Hall and W. Philip Kegelmeyer. SMOTE: Synthetic Minority Over-sampling Technique. J. Artif. Intell. Res. (JAIR, 16. 2002. [View Context].
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].
Ramesh Natarajan and Edwin P D Pednault. Segmented Regression Estimators for Massive Data Sets. SDM. 2002. [View Context].
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].
Stephen D. Bay and Michael J. Pazzani. Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl. Discov, 5. 2001. [View Context].
Dmitry Pavlov and Jianchang Mao and Byron Dom. Scaling-Up Support Vector Machines Using Boosting Algorithm. ICPR. 2000. [View Context].
Gary M. Weiss and Haym Hirsh. A Quantitative Study of Small Disjuncts: Experiments and Results. Department of Computer Science Rutgers University. 2000. [View Context].
Dmitry Pavlov and Darya Chudova and Padhraic Smyth. Towards scalable support vector machines using squashing. KDD. 2000. [View Context].
Kristin P. Bennett and Ayhan Demiriz and John Shawe-Taylor. A Column Generation Algorithm For Boosting. ICML. 2000. [View Context].
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].
Jie Cheng and Russell Greiner. Comparing Bayesian Network Classifiers. UAI. 1999. [View Context].
Yk Huhtala and Juha Kärkkäinen and Pasi Porkka and Hannu Toivonen. Efficient Discovery of Functional and Approximate Dependencies Using Partitions. ICDE. 1998. [View Context].
John C. Platt. Using Analytic QP and Sparseness to Speed Training of Support Vector Machines. NIPS. 1998. [View Context].
Ron Kohavi. Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. KDD. 1996. [View Context].
William W. Cohen and Yoram Singer. A Simple, Fast, and Effective Rule Learner. AT&T Labs--Research Shannon Laboratory. [View Context].
Haixun Wang and Philip S. Yu. SSDT-NN: A Subspace-Splitting Decision Tree Classifier with Application to Target Selection. IBM T. J. Watson Research Center. [View Context].
S. V. N Vishwanathan and Alexander J. Smola and M. Narasimha Murty. considerably faster than competing methods such as Sequential Minimal Optimization or the Nearest Point Algorithm. Machine Learning Program, National ICT for Australia. [View Context].
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].
Rong-En Fan and P. -H Chen and C. -J Lin. Working Set Selection Using the Second Order Information for Training SVM. Department of Computer Science and Information Engineering National Taiwan University. [View Context].
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].
Ron Kohavi and Barry G. Becker and Dan Sommerfield. Improving Simple Bayes. Data Mining and Visualization Group Silicon Graphics, Inc. [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].
David R. Musicant. DATA MINING VIA MATHEMATICAL PROGRAMMING AND MACHINE LEARNING. Doctor of Philosophy (Computer Sciences) UNIVERSITY. [View Context].
Please refer to the Machine Learning
Repository's citation policy