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174 lines
7.1 KiB
Plaintext
Executable File
174 lines
7.1 KiB
Plaintext
Executable File
% 1. Title: Hayes-Roth & Hayes-Roth (1977) Database
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% 2. Source Information:
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% (a) Creators: Barbara and Frederick Hayes-Roth
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% (b) Donor: David W. Aha (aha@ics.uci.edu) (714) 856-8779
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% (c) Date: March, 1989
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%
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% 3. Past Usage:
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% 1. Hayes-Roth, B., & Hayes-Roth, F. (1977). Concept learning and the
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% recognition and classification of exemplars. Journal of Verbal Learning
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% and Verbal Behavior, 16, 321-338.
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% -- Results:
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% -- Human subjects classification and recognition performance:
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% 1. decreases with distance from the prototype,
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% 2. is better on unseen prototypes than old instances, and
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% 3. improves with presentation frequency during learning.
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% 2. Anderson, J.R., & Kline, P.J. (1979). A learning system and its
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% psychological implications. In Proceedings of the Sixth International
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% Joint Conference on Artificial Intelligence (pp. 16-21). Tokyo, Japan:
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% Morgan Kaufmann.
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% -- Partitioned the results into 4 classes:
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% 1. prototypes
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% 2. near-prototypes with high presentation frequency during learning
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% 3. near-prototypes with low presentation frequency during learning
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% 4. instances that are far from protoypes
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% -- Described evidence that ACT's classification confidence and
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% recognition behaviors closely simulated human subjects' behaviors.
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% 3. Aha, D.W. (1989). Incremental learning of independent, overlapping, and
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% graded concept descriptions with an instance-based process framework.
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% Manuscript submitted for publication.
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% -- Used same partition as Anderson & Kline
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% -- Described evidence that Bloom's classification confidence behavior
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% is similar to the human subjects' behavior. Bloom fitted the data
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% more closely than did ACT.
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%
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% 4. Relevant Information:
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% This database contains 5 numeric-valued attributes. Only a subset of
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% 3 are used during testing (the latter 3). Furthermore, only 2 of the
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% 3 concepts are "used" during testing (i.e., those with the prototypes
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% 000 and 111). I've mapped all values to their zero-indexing equivalents.
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%
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% Some instances could be placed in either category 0 or 1. I've followed
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% the authors' suggestion, placing them in each category with equal
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% probability.
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%
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% I've replaced the actual values of the attributes (i.e., hobby has values
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% chess, sports and stamps) with numeric values. I think this is how
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% the authors' did this when testing the categorization models described
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% in the paper. I find this unfair. While the subjects were able to bring
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% background knowledge to bear on the attribute values and their
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% relationships, the algorithms were provided with no such knowledge. I'm
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% uncertain whether the 2 distractor attributes (name and hobby) are
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% presented to the authors' algorithms during testing. However, it is clear
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% that only the age, educational status, and marital status attributes are
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% given during the human subjects' transfer tests.
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%
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% 5. Number of Instances: 132 training instances, 28 test instances
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%
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% 6. Number of Attributes: 5 plus the class membership attribute. 3 concepts.
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%
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% 7. Attribute Information:
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% -- 1. name: distinct for each instance and represented numerically
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% -- 2. hobby: nominal values ranging between 1 and 3
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% -- 3. age: nominal values ranging between 1 and 4
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% -- 4. educational level: nominal values ranging between 1 and 4
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% -- 5. marital status: nominal values ranging between 1 and 4
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% -- 6. class: nominal value between 1 and 3
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%
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% 9. Missing Attribute Values: none
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%
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% 10. Class Distribution: see below
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%
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% 11. Detailed description of the experiment:
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% 1. 3 categories (1, 2, and neither -- which I call 3)
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% -- some of the instances could be classified in either class 1 or 2, and
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% they have been evenly distributed between the two classes
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% 2. 5 Attributes
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% -- A. name (a randomly-generated number between 1 and 132)
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% -- B. hobby (a randomly-generated number between 1 and 3)
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% -- C. age (a number between 1 and 4)
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% -- D. education level (a number between 1 and 4)
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% -- E. marital status (a number between 1 and 4)
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% 3. Classification:
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% -- only attributes C-E are diagnostic; values for A and B are ignored
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% -- Class Neither: if a 4 occurs for any attribute C-E
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% -- Class 1: Otherwise, if (# of 1's)>(# of 2's) for attributes C-E
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% -- Class 2: Otherwise, if (# of 2's)>(# of 1's) for attributes C-E
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% -- Either 1 or 2: Otherwise, if (# of 2's)=(# of 1's) for attributes C-E
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% 4. Prototypes:
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% -- Class 1: 111
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% -- Class 2: 222
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% -- Class Either: 333
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% -- Class Neither: 444
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% 5. Number of training instances: 132
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% -- Each instance presented 0, 1, or 10 times
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% -- None of the prototypes seen during training
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% -- 3 instances from each of categories 1, 2, and either are repeated
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% 10 times each
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% -- 3 additional instances from the Either category are shown during
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% learning
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% 5. Number of test instances: 28
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% -- All 9 class 1
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% -- All 9 class 2
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% -- All 6 class Either
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% -- All 4 prototypes
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% --------------------
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% -- 28 total
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%
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% Observations of interest:
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% 1. Relative classification confidence of
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% -- prototypes for classes 1 and 2 (2 instances)
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% (Anderson calls these Class 1 instances)
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% -- instances of class 1 with frequency 10 during training and
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% instances of class 2 with frequency 10 during training that
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% are 1 value away from their respective prototypes (6 instances)
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% (Anderson calls these Class 2 instances)
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% -- instances of class 1 with frequency 1 during training and
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% instances of class 2 with frequency 1 during training that
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% are 1 value away from their respective prototypes (6 instances)
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% (Anderson calls these Class 3 instances)
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% -- instances of class 1 with frequency 1 during training and
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% instances of class 2 with frequency 1 during training that
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% are 2 values away from their respective prototypes (6 instances)
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% (Anderson calls these Class 4 instances)
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% 2. Relative classification recognition of them also
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%
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% Some Expected results:
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% Both frequency and distance from prototype will effect the classification
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% accuracy of instances. Greater the frequency, higher the classification
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% confidence. Closer to prototype, higher the classification confidence.
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%
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% Information about the dataset
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% CLASSTYPE: nominal
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% CLASSINDEX: last
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%
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@relation hayes-roth
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@attribute hobby INTEGER
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@attribute age INTEGER
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@attribute educational_level INTEGER
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@attribute marital_status INTEGER
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@attribute class {1,2,3,4}
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@data
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1,1,1,2,1
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1,1,2,1,1
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1,2,1,1,1
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1,1,1,3,1
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1,1,3,1,1
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1,3,1,1,1
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1,1,3,3,1
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1,3,1,3,1
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1,3,3,1,1
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1,2,2,1,2
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1,2,1,2,2
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1,1,2,2,2
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1,2,2,3,2
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1,2,3,2,2
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1,3,2,2,2
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1,2,3,3,2
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1,3,2,3,2
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1,3,3,2,2
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1,1,3,2,1
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1,3,2,1,2
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1,2,1,3,1
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1,2,3,1,2
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1,1,2,3,1
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1,3,1,2,2
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1,1,1,1,1
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1,2,2,2,2
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1,3,3,3,1
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1,4,4,4,3
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