isolet
OpenML dataset with id 44135
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22103260/isolet.arff
Upload date: 5 July 2022
Dataset Characteristics
Number of classes: 0
Number of features: 614 (numeric: 614, symbolic: 0 and in total binary: 0 )
Number of instances: 7,797
Number of instances with missing values: 0
Number of missing values: 0
Dataset used in the tabular data benchmark https://github.com/LeoGrin/tabular-benchmark, transformed in the same way. This dataset belongs to the "regression on numerical features" benchmark. Original description:
Author: Ron Cole and Mark Fanty (cole@cse.ogi.edu, fanty@cse.ogi.edu) Donor: Tom Dietterich (tgd@cs.orst.edu) Source: UCI Please cite: UCI
Description
ISOLET (Isolated Letter Speech Recognition) dataset was generated as follows: 150 subjects spoke the name of each letter of the alphabet twice. Hence, there are 52 training examples from each speaker. The speakers are grouped into sets of 30 speakers each, 4 groups can serve as training set, the last group as the test set. A total of 3 examples are missing, the authors dropped them due to difficulties in recording.
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!
Source
- Creators:
Ron Cole and Mark Fanty Department of Computer Science and Engineering, Oregon Graduate Institute, Beaverton, OR 97006. cole '@' cse.ogi.edu, fanty '@' cse.ogi.edu
- Donor:
Tom Dietterich Department of Computer Science Oregon State University, Corvallis, OR 97331 tgd '@' cs.orst.edu
Attributes Information
All attributes are continuous, real-valued attributes scaled into the range -1.0 to 1.0. The features are described in the paper by Cole and Fanty cited below. The features include spectral coefficients; contour features, sonorant features, pre-sonorant features, and post-sonorant features. The exact order of appearance of the features is not known.
Relevant papers
Fanty, M., Cole, R. (1991). Spoken letter recognition. In Lippman, R. P., Moody, J., and Touretzky, D. S. (Eds). Advances in Neural Information Processing Systems 3. San Mateo, CA: Morgan Kaufmann.
Dietterich, T. G., Bakiri, G. (1991) Error-correcting output codes: A general method for improving multiclass inductive learning programs. Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91), Anaheim, CA: AAAI Press.
Dietterich, T. G., Bakiri, G. (1994) Solving Multiclass Learning Problems via Error-Correcting Output Codes.
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