ISOLET
Donated on 9/11/1994
Goal: Predict which letter-name was spoken--a simple classification task.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Classification
Feature Type
Real
# Instances
7797
# Features
617
Dataset Information
Additional Information
This data set was generated as follows. 150 subjects spoke the name of each letter of the alphabet twice. Hence, we have 52 training examples from each speaker. The speakers are grouped into sets of 30 speakers each, and are referred to as isolet1, isolet2, isolet3, isolet4, and isolet5. The data appears in isolet1+2+3+4.data in sequential order, first the speakers from isolet1, then isolet2, and so on. The test set, isolet5, is a separate file. You will note that 3 examples are missing. I believe they were dropped due to difficulties in recording. I believe this is a good domain for a noisy, perceptual task. It is also a very good domain for testing the scaling abilities of algorithms. For example, C4.5 on this domain is slower than backpropagation! I have formatted the data for C4.5 and provided a C4.5-style names file as well.
Has Missing Values?
No
Introductory Paper
By Fanty, M., Cole, R.. 1991
Published in Advances in Neural Information Processing Systems 3
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| Attribute1 | Feature | Continuous | no | ||
| Attribute2 | Feature | Continuous | no | ||
| Attribute3 | Feature | Continuous | no | ||
| Attribute4 | Feature | Continuous | no | ||
| Attribute5 | Feature | Continuous | no | ||
| Attribute6 | Feature | Continuous | no | ||
| Attribute7 | Feature | Continuous | no | ||
| Attribute8 | Feature | Continuous | no | ||
| Attribute9 | Feature | Continuous | no | ||
| Attribute10 | Feature | Continuous | no |
0 to 10 of 618
Additional Variable Information
The features are described in the paper by Cole and Fanty cited above. The features include spectral coefficients; contour features, sonorant features, pre-sonorant features, and post-sonorant features. Exact order of appearance of the features is not known.
Baseline Model Performance
Dataset Files
| File | Size |
|---|---|
| isolet1+2+3+4.data.Z | 7.7 MB |
| isolet5.data.Z | 1.9 MB |
| isolet.names | 10.8 KB |
| isolet.info | 7.4 KB |
| Index | 189 Bytes |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset isolet = fetch_ucirepo(id=54) # data (as pandas dataframes) X = isolet.data.features y = isolet.data.targets # metadata print(isolet.metadata) # variable information print(isolet.variables)
Cole, R. & Fanty, M. (1991). ISOLET [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C51G69.
Creators
Ron Cole
Mark Fanty
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.