View datasets-UCI vowel (public)
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- unknown (from Weka repository)
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- arff slurped Weka
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- Floating Point,String
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# Instances: 990 / # Attributes: 14
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- Original Data Format
- arff
- Name
- vowel
- Version mldata
- 0
- Comment
Introduction ============In my work on context-sensitive learning, I used the "Deterding Vowel Recognition Data", but I found it necessary to reformulate the data. Implicit in the original data is contextual information on the speaker's gender and identity. For my work, it was necessary to make this information explicit. The file "vowel-context.data" adds the speaker's sex and identity as new features. The format of the data file is described below.
Peter Turney peter@ai.iit.nrc.ca
References ==========P. Turney. "Robust Classification With Context-Sensitive Features." Proceedings of the Sixth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems (IEA/AIE-93): 268-276. 1993.
URL: ftp://ai.iit.nrc.ca/pub/ksl-papers/NRC-35074.ps.Z
P. Turney. "Exploiting Context When Learning to Classify." Proceedings of the European Conference on Machine Learning (ECML-93): 402-407. 1993.
URL: ftp://ai.iit.nrc.ca/pub/ksl-papers/NRC-35058.ps.Z
File Structure ============== Column Description ------------------------------- 0 Train or Test 1 Speaker Number 2 Sex 3 Feature 0 4 Feature 1 5 Feature 2 6 Feature 3 7 Feature 4 8 Feature 5 9 Feature 6 10 Feature 7 11 Feature 8 12 Feature 9 13 Class Numerical Codes =============== Speaker Code Number --------------------------- Andrew 0 Bill 1 David 2 Mark 3 Jo 4 Kate 5 Penny 6 Rose 7 Mike 8 Nick 9 Rich 10 Tim 11 Sarah 12 Sue 13 Wendy 14 Set Number --------------------------- Train 0 Test 1 Sex Number --------------------------- Male 0 Female 1 Class Number --------------------------- hid 0 hId 1 hEd 2 hAd 3 hYd 4 had 5 hOd 6 hod 7 hUd 8 hud 9 hed 10 Speaker Code Number Sex Train/Test --------------------------------------------------------------- Andrew 0 0 0 Bill 1 0 0 David 2 0 0 Mark 3 0 0 Jo 4 1 0 Kate 5 1 0 Penny 6 1 0 Rose 7 1 0 Mike 8 0 1 Nick 9 0 1 Rich 10 0 1 Tim 11 0 1 Sarah 12 1 1 Sue 13 1 1 Wendy 14 1 1Num Instances: 990 Num Attributes: 14 Num missing: 0 / 0.0%
name type enum ints real missing distinct (1)1 'Train or Test' Enum 100% 0% 0% 0 / 0% 2 / 0% 0% 2 'Speaker Number' Enum 0% 100% 0% 0 / 0% 15 / 2% 0% 3 'Sex' Enum 0% 100% 0% 0 / 0% 2 / 0% 0% 4 'Feature 0' Real 0% 0% 100% 0 / 0% 853 / 86% 74% 5 'Feature 1' Real 0% 0% 100% 0 / 0% 877 / 89% 78% 6 'Feature 2' Real 0% 0% 100% 0 / 0% 815 / 82% 67% 7 'Feature 3' Real 0% 0% 100% 0 / 0% 836 / 84% 71% 8 'Feature 4' Real 0% 0% 100% 0 / 0% 803 / 81% 66% 9 'Feature 5' Real 0% 0% 100% 0 / 0% 798 / 81% 64% 10 'Feature 6' Real 0% 0% 100% 0 / 0% 748 / 76% 57% 11 'Feature 7' Real 0% 0% 100% 0 / 0% 794 / 80% 64% 12 'Feature 8' Real 0% 0% 100% 0 / 0% 788 / 80% 63% 13 'Feature 9' Real 0% 0% 100% 0 / 0% 775 / 78% 60% 14 'Class' Enum 0% 100% 0% 0 / 0% 11 / 1% 0%
Relabeled values in attribute 'Speaker Number' From: 0 To: Andrew
From: 1 To: Bill
From: 2 To: David
From: 3 To: Mark
From: 4 To: Jo
From: 5 To: Kate
From: 6 To: Penny
From: 7 To: Rose
From: 8 To: Mike
From: 9 To: Nick
From: 10 To: Rich
From: 11 To: Tim
From: 12 To: Sarah
From: 13 To: Sue
From: 14 To: Wendy
Relabeled values in attribute 'Sex' From: 0 To: Male
From: 1 To: Female
Relabeled values in attribute 'Class' From: 0 To: hid
From: 1 To: hId
From: 2 To: hEd
From: 3 To: hAd
From: 4 To: hYd
From: 5 To: had
From: 6 To: hOd
From: 7 To: hod
From: 8 To: hUd
From: 9 To: hud
From: 10 To: hed
- Names
- Train or Test,Speaker Number,Sex,Feature 0,Feature 1,Feature 2,Feature 3,Feature 4,Feature 5,Feature 6,
- Types
- nominal:Train,Test
- nominal:Andrew,Bill,David,Mark,Jo,Kate,Penny,Rose,Mike,Nick,Rich,Tim,Sarah,Sue,Wendy
- nominal:Male,Female
- numeric
- numeric
- numeric
- numeric
- numeric
- numeric
- numeric
- Data (first 10 data points)
Trai... Spea... Sex Feat... Feat... Feat... Feat... Feat... Feat... Feat... ... Train Andrew Male -3.639 0.418 -0.67 1.779 -0.168 1.627 -0.388 ... Train Andrew Male -3.327 0.496 -0.694 1.365 -0.265 1.933 -0.363 ... Train Andrew Male -2.12 0.894 -1.576 0.147 -0.707 1.559 -0.579 ... Train Andrew Male -2.287 1.809 -1.498 1.012 -1.053 1.06 -0.567 ... Train Andrew Male -2.598 1.938 -0.846 1.062 -1.633 0.764 0.394 ... Train Andrew Male -2.852 1.914 -0.755 0.825 -1.588 0.855 0.217 ... Train Andrew Male -3.482 2.524 -0.433 1.048 -1.995 0.902 0.322 ... Train Andrew Male -3.941 2.305 0.124 1.771 -1.815 0.593 -0.435 ... Train Andrew Male -3.86 2.116 -0.939 0.688 -0.675 1.679 -0.512 ... Train Andrew Male -3.648 1.812 -1.378 1.578 0.065 1.577 -0.466 ... ... ... ... ... ... ... ... ... ... ... ...
- Description
A jarfile containing 37 classification problems, originally obtained from the UCI repository (datasets-UCI.jar, 1,190,961 Bytes).
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- Data Source
- http://www.ics.uci.edu/~mlearn/MLRepository.html
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- revision 1
- by mldata on 2010-11-06 09:57
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Acknowledgements
This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
http://www.pascal-network.org/.