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unknown (from Weka repository)

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arff slurped Weka

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Integer,Floating Point,String

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# Instances: 4177 / # Attributes: 9
HDF5 (450.2 KB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

'abalone'

Version mldata

0

Comment

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 (contact: Warwick Nash +61 02 277277, wnash@dpi.tas.gov.au)

(b) Donor of database: Sam Waugh (Sam.Waugh@cs.utas.edu.au) Department of Computer Science, University of Tasmania GPO Box 252C, Hobart, Tasmania 7001, Australia

(c) Date received: December 1995

1. Past Usage:

Sam Waugh (1995) "Extending and benchmarking Cascade-Correlation", PhD 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.

David Clark, Zoltan Schreter, Anthony Adams "A Quantitative Comparison of Dystal and Backpropagation", submitted to the Australian Conference on 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): 64% 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

1. 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_
species) in Tasmania. I. Blacklip Abalone (_H. rubra_) from the North
Coast and Islands of Bass Strait", Sea Fisheries Division, Technical
Report No. 48 (ISSN 1034-3288)

1. Number of Instances: 4177

2. Number of Attributes: 8

3. 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
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
Correl  0.557   0.575   0.557   0.540   0.421   0.504   0.628     1.0

1. Missing Attribute Values: None

2. Class Distribution:

   Class Examples ----- -------- 1 1 2 1 3 15 4 57 5 115 6 259 7 391 8 568 9 689 10 634 11 487 12 267 13 203 14 126 15 103 16 67 17 58 18 42 19 32 20 26 21 14 22 6 23 9 24 2 25 1 26 1 27 2 29 1 ----- ---- Total 4177

Num Instances: 4177 Num Attributes: 9 Num Continuous: 8 (Int 1 / Real 7) Num Discrete: 1 Missing values: 0 / 0.0%

name                      type enum ints real     missing    distinct  (1)

1 'Sex' Enum 100% 0% 0% 0 / 0% 3 / 0% 0% 2 'Length' Real 0% 0% 100% 0 / 0% 134 / 3% 0% 3 'Diameter' Real 0% 0% 100% 0 / 0% 111 / 3% 0% 4 'Height' Real 0% 0% 100% 0 / 0% 51 / 1% 0% 5 'Whole weight' Real 0% 0% 100% 0 / 0% 2429 / 58% 31% 6 'Shucked weight' Real 0% 0% 100% 0 / 0% 1515 / 36% 10% 7 'Viscera weight' Real 0% 0% 100% 0 / 0% 880 / 21% 3% 8 'Shell weight' Real 0% 0% 100% 0 / 0% 926 / 22% 8% 9 'Class_Rings' Int 0% 100% 0% 0 / 0% 28 / 1% 0%

Names

Sex,Length,Diameter,Height,Whole weight,Shucked weight,Viscera weight,Shell weight,Class_Rings,

Types

1. nominal:M,F,I
2. numeric
3. numeric
4. numeric
5. numeric
6. numeric
7. numeric
8. numeric
9. numeric

Data (first 10 data points)

Sex	Length	Diam...	Height	Whol...	Shuc...	Visc...	Shel...	Clas...
M	0.455	0.365	0.095	0.514	0.2245	0.101	0.15	15
M	0.35	0.265	0.09	0.2255	0.0995	0.0485	0.07	7
F	0.53	0.42	0.135	0.677	0.2565	0.1415	0.21	9
M	0.44	0.365	0.125	0.516	0.2155	0.114	0.155	10
I	0.33	0.255	0.08	0.205	0.0895	0.0395	0.055	7
I	0.425	0.3	0.095	0.3515	0.141	0.0775	0.12	8
F	0.53	0.415	0.15	0.7775	0.237	0.1415	0.33	20
F	0.545	0.425	0.125	0.768	0.294	0.1495	0.26	16
M	0.475	0.37	0.125	0.5095	0.2165	0.1125	0.165	9
F	0.55	0.44	0.15	0.8945	0.3145	0.151	0.32	19
...	...	...	...	...	...	...	...	...

Description

A jarfile containing 30 regression datasets collected by Luis Torgo (regression-datasets.jar, 10,090,266 Bytes).

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revision 1

by mldata on 2010-11-06 09:58

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