abalone数据介绍

1. Title of Database: Abalone data

2. Sources:

(a) Original owners of database:

Marine Resources Division

Marine Research Laboratories - Taroona

Department of Primary Industry and Fisheries, Tasmania

GPO Box 619F, Hobart, Tasmania 7001, Australia

(b) Donor of database:

Department of Computer Science, University of Tasmania

GPO Box 252C, Hobart, Tasmania 7001, Australia

(c) Date received: December 1995

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3. Past Usage:

Sam Waugh (1995) \"Extending and benchmarking Cascade-Correlation\

thesis, Computer Science Department, University of Tasmania.

-- Test set performance (final 1044 examples, first 3133 used for training):

24.86% Cascade-Correlation (no hidden nodes)

26.25% Cascade-Correlation (5 hidden nodes)

21.5% C4.5

0.0% Linear Discriminate Analysis

3.57% k=5 Nearest Neighbour

(Problem encoded as a classification task)

-- Data set samples are highly overlapped. Further information is required

to separate completely using affine combinations. Other restrictions

to data set examined.

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David Clark, Zoltan Schreter, Anthony Adams \"A Quantitative Comparison of

Dystal and Backpropagation\

Neural Networks (ACNN'96). Data set treated as a 3-category classification

problem (grouping ring classes 1-8, 9 and 10, and 11 on).

-- Test set performance (3133 training, 1044 testing as above):

% Backprop

55% Dystal

-- Previous work (Waugh, 1995) on same data set:

61.40% Cascade-Correlation (no hidden nodes)

65.61% Cascade-Correlation (5 hidden nodes)

59.2% C4.5

32.57% Linear Discriminate Analysis

62.46% k=5 Nearest Neighbour

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4. Relevant Information Paragraph:

Predicting the age of abalone from physical measurements. The age of

abalone is determined by cutting the shell through the cone, staining it,

and counting the number of rings through a microscope -- a boring and

time-consuming task. Other measurements, which are easier to obtain, are

used to predict the age. Further information, such as weather patterns

and location (hence food availability) may be required to solve the problem.

From the original data examples with missing values were removed (the

majority having the predicted value missing), and the ranges of the

continuous values have been scaled for use with an ANN (by dividing by 200).

Data comes from an original (non-machine-learning) study:

Warwick J Nash, Tracy L Sellers, Simon R Talbot, Andrew J Cawthorn and

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

Coast and Islands of Bass Strait\

Report No. 48 (ISSN 1034-3288)

5. Number of Instances: 4177

6. Number of Attributes: 8

7. Attribute information:

Given is the attribute name, attribute type, the measurement unit and a

brief description. The number of rings is the value to predict: either

as a continuous value or as a classification problem.

Name Data Type Meas. Description

---- --------- ----- -----------

Sex nominal M, F, and I (infant)

Length continuous mm Longest shell measurement

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Diameter continuous mm perpendicular to length

Height continuous mm with meat in shell

Whole weight continuous grams whole abalone

Shucked weight continuous grams weight of meat

Viscera weight continuous grams gut weight (after bleeding)

Shell weight continuous grams after being dried

Rings integer +1.5 gives the age in years

Statistics for numeric domains:

Length Diam Height Whole Shucked Viscera Shell Rings

Min 0.075 0.055 0.000 0.002 0.001 0.001 0.002 1

Max 0.815 0.650 1.130 2.826 1.488 0.760 1.005 29

Mean 0.524 0.408 0.140 0.829 0.359 0.181 0.239 9.934

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 0.0 0.421 0.504 0.628 1.0

8. Missing Attribute Values: None

9. Class Distribution:

Class Examples

----- --------

1 1

2 1

3 15

4 57

5 115

6 259

7 391

8 568

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9 6

10 634

11 487

12 267

13 203

14 126

15 103

16 67

17 58

18 42

19 32

20 26

21 14

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22 6

23 9

24 2

25 1

26 1

27 2

29 1

----- ----

Total 4177

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