Description

This database contains 5 numeric-valued attributes. Only a subset of 3 are used during testing (the latter 3). Furthermore, only 2 of the 3 concepts are "used" during testing (i.e., those with the prototypes 000 and 111). I've mapped all values to their zero-indexing equivalents. Some instances could be placed in either category 0 or 1. I've followed the authors' suggestion, placing them in each category with equal probability. I've replaced the actual values of the attributes (i.e., hobby has values chess, sports and stamps) with numeric values. I think this is how the authors' did this when testing the categorization models described in the paper. I find this unfair. While the subjects were able to bring background knowledge to bear on the attribute values and their relationships, the algorithms were provided with no such knowledge. I'm uncertain whether the 2 distractor attributes (name and hobby) are presented to the authors' algorithms during testing. However, it is clear that only the age, educational status, and marital status attributes are given during the human subjects' transfer tests.

Related Papers

  • Jerome H. Friedman and Ron Kohavi and Youngkeol Yun. To appear in AAAI-96 Lazy Decision Trees. Statistics Department and Stanford Linear Accelerator Center Stanford University. [link]
  • Gabor Melli. A Lazy Model-Based Approach to On-Line Classification. University of British Columbia. 1989. [link]
  • Yuan Jiang and Zhi-Hua Zhou. Editing Training Data for kNN Classifiers with Neural Network Ensemble. ISNN (1). 2004. [link]
  • Bob Ricks and Dan Ventura. Training a Quantum Neural Network. NIPS. 2003. [link]
  • Anthony D. Griffiths and Derek Bridge. A Yardstick for the Evaluation of Case-Based Classifiers. Department of Computer Science, University of York. [link]
  • [link]

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