Data
abalone

abalone

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Make target (age) numericAuthor: Source: Unknown - Please cite: 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 3. 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 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_ 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) 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 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 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 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%

9 features

Class_number_of_rings (target)numeric28 unique values
0 missing
Sexnominal3 unique values
0 missing
Lengthnumeric134 unique values
0 missing
Diameternumeric111 unique values
0 missing
Heightnumeric51 unique values
0 missing
Whole_weightnumeric2429 unique values
0 missing
Shucked_weightnumeric1515 unique values
0 missing
Viscera_weightnumeric880 unique values
0 missing
Shell_weightnumeric926 unique values
0 missing

19 properties

4177
Number of instances (rows) of the dataset.
9
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
8
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of instances having missing values.
-1.1
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
88.89
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
11.11
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.

2 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: root_mean_squared_error - target_feature: Class_number_of_rings
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class_number_of_rings
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