Data
isolet

isolet

active ARFF Publicly available Visibility: public Uploaded 16-06-2022 by Leo Grin
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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](https://archive.ics.uci.edu/ml/datasets/ISOLET) 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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f498numeric3379 unique values
0 missing
f243numeric845 unique values
0 missing
f499numeric3461 unique values
0 missing
f244numeric802 unique values
0 missing
f500numeric3509 unique values
0 missing
f245numeric787 unique values
0 missing
f501numeric3559 unique values
0 missing
f246numeric788 unique values
0 missing
f502numeric3564 unique values
0 missing
f247numeric798 unique values
0 missing
f503numeric3672 unique values
0 missing
f248numeric806 unique values
0 missing
f504numeric3769 unique values
0 missing
f249numeric808 unique values
0 missing
f505numeric3840 unique values
0 missing
f250numeric817 unique values
0 missing
f506numeric3862 unique values
0 missing
f251numeric847 unique values
0 missing
f507numeric3816 unique values
0 missing
f252numeric862 unique values
0 missing
f508numeric3826 unique values
0 missing
f253numeric861 unique values
0 missing
f509numeric3840 unique values
0 missing
f254numeric900 unique values
0 missing
f510numeric3885 unique values
0 missing
f255numeric917 unique values
0 missing
f511numeric3811 unique values
0 missing
f256numeric958 unique values
0 missing
f512numeric3811 unique values
0 missing

19 properties

7797
Number of instances (rows) of the dataset.
614
Number of attributes (columns) of the dataset.
26
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
613
Number of numeric attributes.
1
Number of nominal attributes.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.3
Average class difference between consecutive instances.
0
Percentage of missing values.
0.08
Number of attributes divided by the number of instances.
99.84
Percentage of numeric attributes.
3.85
Percentage of instances belonging to the most frequent class.
0.16
Percentage of nominal attributes.
300
Number of instances belonging to the most frequent class.
3.82
Percentage of instances belonging to the least frequent class.
298
Number of instances belonging to the least frequent class.

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