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diff --git a/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone.names b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone.names new file mode 100644 index 00000000..8fbe142d --- /dev/null +++ b/sourcecodes/bnt-master/SLP/examples/UCI_DataSets/abalone.names @@ -0,0 +1,145 @@ +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 |