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JapaneseVowels

JapaneseVowels

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Author: Mineichi Kudo, Jun Toyama, Masaru Shimbo ({mine,jun,shimbo}@main.eng.hokudai.ac.jp) Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Japanese+Vowels) - 2000 Please cite: Japanese vowels This dataset records 640 time series of 12 LPC cepstrum coefficients taken from nine male speakers. The data was collected for examining our newly developed classifier for multidimensional curves (multidimensional time series). Nine male speakers uttered two Japanese vowels /ae/ successively. For each utterance, with the analysis parameters described below, we applied 12-degree linear prediction analysis to it to obtain a discrete-time series with 12 LPC cepstrum coefficients. This means that one utterance by a speaker forms a time series whose length is in the range 7-29 and each point of a time series is of 12 features (12 coefficients). Similar data are available for different utterances /ei/, /iu/, /uo/, /oa/ in addition to /ae/. Please contact the donor if you are interested in using this data. The number of the time series is 640 in total. We used one set of 270 time series for training and the other set of 370 time series for testing. Analysis parameters: * Sampling rate : 10kHz * Frame length : 25.6 ms * Shift length : 6.4ms * Degree of LPC coefficients : 12 Each line represents 12 LPC coefficients in the increasing order separated by spaces. This corresponds to one analysis frame. Lines are organized into blocks, which are a set of 7-29 lines separated by blank lines and corresponds to a single speech utterance of /ae/ with 7-29 frames. Each speaker is a set of consecutive blocks. In ae.train there are 30 blocks for each speaker. Blocks 1-30 represent speaker 1, blocks 31-60 represent speaker 2, and so on up to speaker 9. In ae.test, speakers 1 to 9 have the corresponding number of blocks: 31 35 88 44 29 24 40 50 29. Thus, blocks 1-31 represent speaker 1 (31 utterances of /ae/), blocks 32-66 represent speaker 2 (35 utterances of /ae/), and so on. Past Usage M. Kudo, J. Toyama and M. Shimbo. (1999). "Multidimensional Curve Classification Using Passing-Through Regions". Pattern Recognition Letters, Vol. 20, No. 11--13, pages 1103--1111. If you publish any work using the dataset, please inform the donor. Use for commercial purposes requires donor permission. References 1. http://ips9.main.eng.hokudai.ac.jp/index_e.html 2. mailto:mine@main.eng.hokudai.ac.jp 3. mailto:jun@main.eng.hokudai.ac.jp 4. mailto:shimbo@main.eng.hokudai.ac.jp 5. http://kdd.ics.uci.edu/ 6. http://www.ics.uci.edu/ 7. http://www.uci.edu/

15 features

speaker (target)nominal9 unique values
0 missing
utterancenumeric88 unique values
0 missing
framenumeric29 unique values
0 missing
coefficient1numeric9935 unique values
0 missing
coefficient2numeric9924 unique values
0 missing
coefficient3numeric9918 unique values
0 missing
coefficient4numeric9906 unique values
0 missing
coefficient5numeric9922 unique values
0 missing
coefficient6numeric9898 unique values
0 missing
coefficient7numeric9876 unique values
0 missing
coefficient8numeric9893 unique values
0 missing
coefficient9numeric9892 unique values
0 missing
coefficient10numeric9857 unique values
0 missing
coefficient11numeric9831 unique values
0 missing
coefficient12numeric9846 unique values
0 missing

107 properties

9961
Number of instances (rows) of the dataset.
15
Number of attributes (columns) of the dataset.
9
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.
14
Number of numeric attributes.
1
Number of nominal attributes.
1
Average class difference between consecutive instances.
0.93
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
0.15
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
0.83
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
0.93
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
0.15
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
0.83
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
0.93
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
0.15
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
0.83
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
3.13
Entropy of the target attribute values.
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.74
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.14
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.14
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
16.2
Percentage of instances belonging to the most frequent class.
1614
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
3.8
Maximum kurtosis among attributes of the numeric type.
20.36
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
9
The maximum number of distinct values among attributes of the nominal type.
1.7
Maximum skewness among attributes of the numeric type.
15.88
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
-0.04
Mean kurtosis among attributes of the numeric type.
2.05
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
9
Average number of distinct values among the attributes of the nominal type.
0.11
Mean skewness among attributes of the numeric type.
1.71
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-0.86
Minimum kurtosis among attributes of the numeric type.
-0.53
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
9
The minimal number of distinct values among attributes of the nominal type.
-0.38
Minimum skewness among attributes of the numeric type.
0.1
Minimum standard deviation of attributes of the numeric type.
7.85
Percentage of instances belonging to the least frequent class.
782
Number of instances belonging to the least frequent class.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.14
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
93.33
Percentage of numeric attributes.
6.67
Percentage of nominal attributes.
First quartile of entropy among attributes.
-0.48
First quartile of kurtosis among attributes of the numeric type.
-0.21
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
-0.27
First quartile of skewness among attributes of the numeric type.
0.15
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
-0.22
Second quartile (Median) of kurtosis among attributes of the numeric type.
-0.04
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.07
Second quartile (Median) of skewness among attributes of the numeric type.
0.23
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
-0.14
Third quartile of kurtosis among attributes of the numeric type.
0.37
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.24
Third quartile of skewness among attributes of the numeric type.
0.41
Third quartile of standard deviation of attributes of the numeric type.
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.16
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.95
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.81
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.16
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.02
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: speaker
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: speaker
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: speaker
0 runs - estimation_procedure: Leave one out - target_feature: speaker
0 runs - estimation_procedure: 10% Holdout set - target_feature: speaker
0 runs - estimation_procedure: 33% Holdout set - target_feature: speaker
0 runs - estimation_procedure: Test on Training Data - target_feature: speaker
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: speaker
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: speaker
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: speaker
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: speaker
Define a new task