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data/tanveer/abalone/abalone.names
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data/tanveer/abalone/abalone.names
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1. Title of Database: Abalone data
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2. Sources:
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(a) Original owners of database:
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Marine Resources Division
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Marine Research Laboratories - Taroona
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Department of Primary Industry and Fisheries, Tasmania
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GPO Box 619F, Hobart, Tasmania 7001, Australia
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(contact: Warwick Nash +61 02 277277, wnash@dpi.tas.gov.au)
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(b) Donor of database:
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Sam Waugh (Sam.Waugh@cs.utas.edu.au)
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Department of Computer Science, University of Tasmania
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GPO Box 252C, Hobart, Tasmania 7001, Australia
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(c) Date received: December 1995
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3. Past Usage:
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Sam Waugh (1995) "Extending and benchmarking Cascade-Correlation", PhD
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thesis, Computer Science Department, University of Tasmania.
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-- Test set performance (final 1044 examples, first 3133 used for training):
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24.86% Cascade-Correlation (no hidden nodes)
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26.25% Cascade-Correlation (5 hidden nodes)
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21.5% C4.5
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0.0% Linear Discriminate Analysis
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3.57% k=5 Nearest Neighbour
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(Problem encoded as a classification task)
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-- Data set samples are highly overlapped. Further information is required
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to separate completely using affine combinations. Other restrictions
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to data set examined.
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David Clark, Zoltan Schreter, Anthony Adams "A Quantitative Comparison of
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Dystal and Backpropagation", submitted to the Australian Conference on
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Neural Networks (ACNN'96). Data set treated as a 3-category classification
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problem (grouping ring classes 1-8, 9 and 10, and 11 on).
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-- Test set performance (3133 training, 1044 testing as above):
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64% Backprop
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55% Dystal
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-- Previous work (Waugh, 1995) on same data set:
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61.40% Cascade-Correlation (no hidden nodes)
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65.61% Cascade-Correlation (5 hidden nodes)
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59.2% C4.5
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32.57% Linear Discriminate Analysis
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62.46% k=5 Nearest Neighbour
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4. Relevant Information Paragraph:
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Predicting the age of abalone from physical measurements. The age of
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abalone is determined by cutting the shell through the cone, staining it,
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and counting the number of rings through a microscope -- a boring and
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time-consuming task. Other measurements, which are easier to obtain, are
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used to predict the age. Further information, such as weather patterns
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and location (hence food availability) may be required to solve the problem.
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From the original data examples with missing values were removed (the
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majority having the predicted value missing), and the ranges of the
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continuous values have been scaled for use with an ANN (by dividing by 200).
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Data comes from an original (non-machine-learning) study:
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Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and
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Wes B Ford (1994) "The Population Biology of Abalone (_Haliotis_
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species) in Tasmania. I. Blacklip Abalone (_H. rubra_) from the North
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Coast and Islands of Bass Strait", Sea Fisheries Division, Technical
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Report No. 48 (ISSN 1034-3288)
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5. Number of Instances: 4177
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6. Number of Attributes: 8
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7. Attribute information:
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Given is the attribute name, attribute type, the measurement unit and a
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brief description. The number of rings is the value to predict: either
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as a continuous value or as a classification problem.
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Name Data Type Meas. Description
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---- --------- ----- -----------
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Sex nominal M, F, and I (infant)
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Length continuous mm Longest shell measurement
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Diameter continuous mm perpendicular to length
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Height continuous mm with meat in shell
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Whole weight continuous grams whole abalone
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Shucked weight continuous grams weight of meat
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Viscera weight continuous grams gut weight (after bleeding)
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Shell weight continuous grams after being dried
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Rings integer +1.5 gives the age in years
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Statistics for numeric domains:
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Length Diam Height Whole Shucked Viscera Shell Rings
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Min 0.075 0.055 0.000 0.002 0.001 0.001 0.002 1
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Max 0.815 0.650 1.130 2.826 1.488 0.760 1.005 29
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Mean 0.524 0.408 0.140 0.829 0.359 0.181 0.239 9.934
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SD 0.120 0.099 0.042 0.490 0.222 0.110 0.139 3.224
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Correl 0.557 0.575 0.557 0.540 0.421 0.504 0.628 1.0
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8. Missing Attribute Values: None
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9. Class Distribution:
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Class Examples
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----- --------
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1 1
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2 1
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3 15
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4 57
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5 115
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6 259
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7 391
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8 568
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9 689
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10 634
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11 487
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12 267
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13 203
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14 126
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15 103
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16 67
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17 58
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18 42
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19 32
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20 26
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21 14
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22 6
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23 9
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24 2
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25 1
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26 1
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27 2
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29 1
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----- ----
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Total 4177
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