Datasets:
Tasks:
Tabular Classification
Sub-tasks:
tabular-multi-class-classification
Size Categories:
n<1K
Language Creators:
expert-generated
Annotations Creators:
no-annotation
Source Datasets:
original
Tags:
License:
| 1. Title of Database: Wine recognition data | |
| Updated Sept 21, 1998 by C.Blake : Added attribute information | |
| 2. Sources: | |
| (a) Forina, M. et al, PARVUS - An Extendible Package for Data | |
| Exploration, Classification and Correlation. Institute of Pharmaceutical | |
| and Food Analysis and Technologies, Via Brigata Salerno, | |
| 16147 Genoa, Italy. | |
| (b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au | |
| (c) July 1991 | |
| 3. Past Usage: | |
| (1) | |
| S. Aeberhard, D. Coomans and O. de Vel, | |
| Comparison of Classifiers in High Dimensional Settings, | |
| Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of | |
| Mathematics and Statistics, James Cook University of North Queensland. | |
| (Also submitted to Technometrics). | |
| The data was used with many others for comparing various | |
| classifiers. The classes are separable, though only RDA | |
| has achieved 100% correct classification. | |
| (RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data)) | |
| (All results using the leave-one-out technique) | |
| In a classification context, this is a well posed problem | |
| with "well behaved" class structures. A good data set | |
| for first testing of a new classifier, but not very | |
| challenging. | |
| (2) | |
| S. Aeberhard, D. Coomans and O. de Vel, | |
| "THE CLASSIFICATION PERFORMANCE OF RDA" | |
| Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of | |
| Mathematics and Statistics, James Cook University of North Queensland. | |
| (Also submitted to Journal of Chemometrics). | |
| Here, the data was used to illustrate the superior performance of | |
| the use of a new appreciation function with RDA. | |
| 4. Relevant Information: | |
| -- These data are the results of a chemical analysis of | |
| wines grown in the same region in Italy but derived from three | |
| different cultivars. | |
| The analysis determined the quantities of 13 constituents | |
| found in each of the three types of wines. | |
| -- I think that the initial data set had around 30 variables, but | |
| for some reason I only have the 13 dimensional version. | |
| I had a list of what the 30 or so variables were, but a.) | |
| I lost it, and b.), I would not know which 13 variables | |
| are included in the set. | |
| -- The attributes are (dontated by Riccardo Leardi, | |
| riclea@anchem.unige.it ) | |
| 1) Alcohol | |
| 2) Malic acid | |
| 3) Ash | |
| 4) Alcalinity of ash | |
| 5) Magnesium | |
| 6) Total phenols | |
| 7) Flavanoids | |
| 8) Nonflavanoid phenols | |
| 9) Proanthocyanins | |
| 10)Color intensity | |
| 11)Hue | |
| 12)OD280/OD315 of diluted wines | |
| 13)Proline | |
| 5. Number of Instances | |
| class 1 59 | |
| class 2 71 | |
| class 3 48 | |
| 6. Number of Attributes | |
| 13 | |
| 7. For Each Attribute: | |
| All attributes are continuous | |
| No statistics available, but suggest to standardise | |
| variables for certain uses (e.g. for us with classifiers | |
| which are NOT scale invariant) | |
| NOTE: 1st attribute is class identifier (1-3) | |
| 8. Missing Attribute Values: | |
| None | |
| 9. Class Distribution: number of instances per class | |
| class 1 59 | |
| class 2 71 | |
| class 3 48 | |