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vowel

vowel

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Author: Source: Unknown - Please cite: * Please use version 2 of this dataset. This version has a train/test feature that should be ignored in OpenML. 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 1 Num 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

14 features

Class (target)nominal11 unique values
0 missing
Train_or_Testnominal2 unique values
0 missing
Speaker_Numbernominal15 unique values
0 missing
Sexnominal2 unique values
0 missing
Feature_0numeric853 unique values
0 missing
Feature_1numeric877 unique values
0 missing
Feature_2numeric815 unique values
0 missing
Feature_3numeric836 unique values
0 missing
Feature_4numeric803 unique values
0 missing
Feature_5numeric798 unique values
0 missing
Feature_6numeric748 unique values
0 missing
Feature_7numeric794 unique values
0 missing
Feature_8numeric788 unique values
0 missing
Feature_9numeric775 unique values
0 missing
Show all 14 features

107 properties

NumberOfInstances
990
Number of instances (rows) of the dataset.
NumberOfFeatures
14
Number of attributes (columns) of the dataset.
NumberOfClasses
11
Number of distinct values of the target attribute (if it is nominal).
NumberOfMissingValues
0
Number of missing values in the dataset.
NumberOfInstancesWithMissingValues
0
Number of instances with at least one value missing.
NumberOfNumericFeatures
10
Number of numeric attributes.
NumberOfSymbolicFeatures
4
Number of nominal attributes.
AutoCorrelation
0
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_DecisionStumpKappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesAUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_NaiveBayesKappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NAUC
0.86
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
CfsSubsetEval_kNN1NKappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk -E "weka.attributeSelection.CfsSubsetEval -P 1 -E 1" -S "weka.attributeSelection.BestFirst -D 1 -N 5" -W
ClassEntropy
3.46
Entropy of the target attribute values.
DecisionStumpAUC
0.68
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.82
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.09
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
Number of attributes divided by the number of instances.
EquivalentNumberOfAtts
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
J48.00001.AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
9.09
Percentage of instances belonging to the most frequent class.
MajorityClassSize
90
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
3.91
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.15
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1.88
Maximum of means among attributes of the numeric type.
MaxMutualInformation
0
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
15
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.36
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1.18
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
1.97
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
-0.39
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
-0.1
Mean of means among attributes of the numeric type.
MeanMutualInformation
0
Average mutual information between the nominal attributes and the target attribute.
MeanNoiseToSignalRatio
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
MeanNominalAttDistinctValues
7.5
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
0.09
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.7
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
1
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.76
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-3.2
Minimum of means among attributes of the numeric type.
MinMutualInformation
0
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
2
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-0.21
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.46
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
9.09
Percentage of instances belonging to the least frequent class.
MinorityClassSize
90
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.42
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.54
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
2
Number of binary attributes.
PercentageOfBinaryFeatures
14.29
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
71.43
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
28.57
Percentage of nominal attributes.
Quartile1AttributeEntropy
1
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
-0.56
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-0.36
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
0
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
-0.01
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.57
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
1
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
-0.43
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
-0.04
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
0
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
0.06
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.63
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
3.91
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
-0.24
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.54
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
0
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.25
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.79
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.64
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.64
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.64
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.3
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.83
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.32
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
6.56
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.08
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.91
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

15 tasks

Supervised Classification on vowel
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Learning Curve on vowel
288 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
Learning Curve on vowel
145 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Data Stream Classification on vowel
25 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
Clustering on vowel
0 runs
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
Clustering on vowel
0 runs - estimation_procedure: 50 times Clustering
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