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| KEEL-dataset - data set description |
This section describes main characteristics of the yeast data set and its attributes:
General information
| Yeast data set | |||
|---|---|---|---|
| Type | Classification | Origin | Real world |
| Features | 8 | (Real / Integer / Nominal) | (8 / 0 / 0) |
| Instances | 1484 | Classes | 10 |
| Missing values? | No | ||
Attribute description
| Attribute | Domain |
|---|---|
| Mcg | [0.11, 1.0] |
| Gvh | [0.13, 1.0] |
| Alm | [0.21, 1.0] |
| Mit | [0.0, 1.0] |
| Erl | [0.5, 1.0] |
| Pox | [0.0, 0.83] |
| Vac | [0.0, 0.73] |
| Nuc | [0.0, 1.0] |
| Class | {MIT, NUC, CYT, ME1, ME2, ME3, EXC, VAC, POX, ERL} |
Additional information
This database contains information about a set of Yeast cells. The task is to determine the localization site of each cell among 10 possible alternatives.
Attributes description:
- Mcg: McGeoch's method for signal sequence recognition.
- Gvh: von Heijne's method for signal sequence recognition.
- Alm: Score of the ALOM membrane spanning region prediction program.
- Mit: Score of discriminant analysis of the amino acid content of the N-terminal region (20 residues long) of mitochondrial and non-mitochondrial proteins.
- Erl: Presence of "HDEL" substring (thought to act as a signal for retention in the endoplasmic reticulum lumen). Binary attribute.
- Pox: Peroxisomal targeting signal in the C-terminus.
- Vac: Score of discriminant analysis of the amino acid content of vacuolar and extracellular proteins.
- Nuc: Score of discriminant analysis of nuclear localization signals of nuclear and non-nuclear proteins.
In this section you can download some files related to the yeast data set:
- The complete data set already formatted in KEEL format can be downloaded from
here
. - A copy of the data set already partitioned by means of a 10-folds cross validation procedure can be downloaded from here.
- A copy of the data set already partitioned by means of a 5-folds cross validation procedure can be downloaded from here.
- The header file associated to this data set can be downloaded from here
. - This is not a native data set from the KEEL project. It has been obtained from the UCI Machine Learning Repository. The original page where the data set can be found is: http://archive.ics.uci.edu/ml/datasets/Yeast.
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