Description

This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute "mpg", 8 of the original instances were removed because they had unknown values for the "mpg" attribute. The original dataset is available in the file "auto-mpg.data-original". "The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993)

Related Papers

  • C. Titus Brown and Harry W. Bullen and Sean P. Kelly and Robert K. Xiao and Steven G. Satterfield and John G. Hagedorn and Judith E. Devaney. Visualization and Data Mining in an 3D Immersive Environment: Summer Project 2003. [link]
  • Thomas Melluish and Craig Saunders and Ilia Nouretdinov and Volodya Vovk and Carol S. Saunders and I. Nouretdinov V.. The typicalness framework: a comparison with the Bayesian approach. Department of Computer Science. 2001. [link]
  • Dan Pelleg and Andrew W. Moore. Mixtures of Rectangles: Interpretable Soft Clustering. ICML. 2001. [link]
  • Zhi-Hua Zhou and Shifu Chen and Zhaoqian Chen. A Statistics Based Approach for Extracting Priority Rules from Trained Neural Networks. IJCNN (3). 2000. [link]
  • Mauro Birattari and Gianluca Bontempi and Hugues Bersini. Lazy Learning Meets the Recursive Least Squares Algorithm. NIPS. 1998. [link]
  • Christopher R. Palmer and Christos Faloutsos. Electricity Based External Similarity of Categorical Attributes. PAKDD. 2003. [link]
  • D. Greig and Hava T. Siegelmann and Michael Zibulevsky. A New Class of Sigmoid Activation Functions That Don't Saturate. 1997. [link]
  • Johannes Furnkranz. Pairwise Classification as an Ensemble Technique. Austrian Research Institute for Artificial Intelligence. [link]
  • Qingping Tao Ph. D. MAKING EFFICIENT LEARNING ALGORITHMS WITH EXPONENTIALLY MANY FEATURES. Qingping Tao A DISSERTATION Faculty of The Graduate College University of Nebraska In Partial Fulfillment of Requirements. 2004. [link]
  • Jinyan Li and Kotagiri Ramamohanarao and Guozhu Dong. Combining the Strength of Pattern Frequency and Distance for Classification. PAKDD. 2001. [link]
  • Dan Pelleg. Scalable and Practical Probability Density Estimators for Scientific Anomaly Detection. School of Computer Science Carnegie Mellon University. 2004. [link]
  • Wai Lam and Kin Keung and Charles X. Ling. PR 1527. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. 2001. [link]
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