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lsvt

lsvt

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Author: Athanasios Tsanas Source: UCI Please cite: A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: Objective automatic assessment of rehabilitative speech treatment in Parkinsons disease, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, January 2014 Dataset title laLSVT Voice Rehabilitation Data Set Source: The dataset was created by Athanasios Tsanas (tsanasthanasis '@' gmail.com) of the University of Oxford. Abstract: 126 samples from 14 participants, 309 features. Aim: assess whether voice rehabilitation treatment lead to phonations considered 'acceptable' or 'unacceptable' (binary class classification problem). Data Set Information: The original paper demonstrated that it is possible to correctly replicate the experts' binary assessment with approximately 90% accuracy using both 10-fold cross-validation and leave-one-subject-out validation. We experimented with both random forests and support vector machines, using standard approaches for optimizing the SVM's hyperparameters. It will be interesting if researchers can improve on this finding using advanced machine learning tools. Details for the dataset can be found on the following paper. A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: “Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, January 2014 A freely available preprint is availabe from the first author's website. Attribute Information: Each attribute (feature) corresponds to the application of a speech signal processing algorithm which aims to characterise objectively the signal. These algorithms include standard perturbation analysis methods, wavelet-based features, fundamental frequency-based features, and tools used to mine nonlinear time-series. Because of the extensive number of attributes we refer the interested readers to the relevant papers for further details. Relevant Papers: The dataset was introduced in: A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: “Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, January 2014 Further details about the speech signal processing algorithms can be found in: A. Tsanas, Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning, D.Phil. (Ph.D.) thesis, University of Oxford, UK, 2012 A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity”, Journal of the Royal Society Interface, Vol. 8, pp. 842-855, 2011 A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “New nonlinear markers and insights into speech signal degradation for effective tracking of Parkinson’s disease symptom severity”, International Symposium on Nonlinear Theory and its Applications (NOLTA), pp. 457-460, Krakow, Poland, 5-8 September 2010 Preprints are available on the first author's website.

311 features

Class (target)nominal2 unique values
0 missing
V1numeric126 unique values
0 missing
V257numeric125 unique values
0 missing
V2numeric126 unique values
0 missing
V258numeric126 unique values
0 missing
V3numeric118 unique values
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V259numeric126 unique values
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V5numeric118 unique values
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V261numeric42 unique values
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V119numeric121 unique values
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V121numeric119 unique values
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V122numeric121 unique values
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V123numeric120 unique values
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V125numeric126 unique values
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V126numeric126 unique values
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V143numeric126 unique values
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V163numeric126 unique values
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V164numeric126 unique values
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V165numeric126 unique values
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V166numeric126 unique values
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V167numeric126 unique values
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V169numeric126 unique values
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V170numeric126 unique values
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V171numeric126 unique values
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V172numeric126 unique values
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V173numeric126 unique values
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V174numeric126 unique values
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V175numeric126 unique values
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V176numeric126 unique values
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V177numeric126 unique values
0 missing
V178numeric126 unique values
0 missing
V179numeric126 unique values
0 missing
V180numeric126 unique values
0 missing
V181numeric126 unique values
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V182numeric126 unique values
0 missing
V183numeric126 unique values
0 missing
V184numeric126 unique values
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V185numeric126 unique values
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V186numeric126 unique values
0 missing
V187numeric126 unique values
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V188numeric126 unique values
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V189numeric126 unique values
0 missing
V190numeric126 unique values
0 missing
V191numeric126 unique values
0 missing
V192numeric126 unique values
0 missing
V193numeric126 unique values
0 missing
V194numeric126 unique values
0 missing
V195numeric126 unique values
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V196numeric126 unique values
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V197numeric126 unique values
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V198numeric126 unique values
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V199numeric126 unique values
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V200numeric126 unique values
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V201numeric126 unique values
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V202numeric126 unique values
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V203numeric126 unique values
0 missing
V204numeric126 unique values
0 missing
V205numeric126 unique values
0 missing
V206numeric126 unique values
0 missing
V207numeric126 unique values
0 missing
V208numeric126 unique values
0 missing
V209numeric126 unique values
0 missing
V210numeric126 unique values
0 missing
V211numeric126 unique values
0 missing
V212numeric126 unique values
0 missing
V213numeric126 unique values
0 missing
V214numeric126 unique values
0 missing
V215numeric126 unique values
0 missing
V216numeric126 unique values
0 missing
V217numeric126 unique values
0 missing
V218numeric126 unique values
0 missing
V219numeric126 unique values
0 missing
V220numeric116 unique values
0 missing
V221numeric15 unique values
0 missing
V222numeric22 unique values
0 missing
V223numeric34 unique values
0 missing
V224numeric47 unique values
0 missing
V225numeric41 unique values
0 missing
V226numeric50 unique values
0 missing
V227numeric65 unique values
0 missing
V228numeric76 unique values
0 missing
V229numeric91 unique values
0 missing
V230numeric97 unique values
0 missing
V231numeric114 unique values
0 missing
V232numeric126 unique values
0 missing
V233numeric126 unique values
0 missing
V234numeric126 unique values
0 missing
V235numeric126 unique values
0 missing
V236numeric126 unique values
0 missing
V237numeric126 unique values
0 missing
V238numeric126 unique values
0 missing
V239numeric126 unique values
0 missing
V240numeric126 unique values
0 missing
V241numeric126 unique values
0 missing
V242numeric126 unique values
0 missing
V243numeric126 unique values
0 missing
V244numeric126 unique values
0 missing
V245numeric126 unique values
0 missing
V246numeric126 unique values
0 missing
V247numeric126 unique values
0 missing
V248numeric126 unique values
0 missing
V249numeric126 unique values
0 missing
V250numeric126 unique values
0 missing
V251numeric23 unique values
0 missing
V252numeric43 unique values
0 missing
V253numeric62 unique values
0 missing
V254numeric108 unique values
0 missing
V255numeric113 unique values
0 missing
V256numeric123 unique values
0 missing

107 properties

126
Number of instances (rows) of the dataset.
311
Number of attributes (columns) of the dataset.
2
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.
310
Number of numeric attributes.
1
Number of nominal attributes.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.39
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
18570761.39
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.23
Second quartile (Median) of skewness among attributes of the numeric type.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
2.47
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
0.32
Percentage of binary attributes.
3.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
2
The maximum number of distinct values among attributes of the nominal type.
-11.03
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
10.84
Maximum skewness among attributes of the numeric type.
0
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
50.01
Third quartile of kurtosis among attributes of the numeric type.
0.34
Average class difference between consecutive instances.
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.25
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
14132861569.78
Maximum standard deviation of attributes of the numeric type.
33.33
Percentage of instances belonging to the least frequent class.
99.68
Percentage of numeric attributes.
50.18
Third quartile of means among attributes of the numeric type.
0.7
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
0.35
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
42
Number of instances belonging to the least frequent class.
0.32
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.25
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
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
27.37
Mean kurtosis among attributes of the numeric type.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
5.73
Third quartile of skewness among attributes of the numeric type.
0.43
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
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.25
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-137218838.85
Mean of means among attributes of the numeric type.
0.4
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.9
First quartile of kurtosis among attributes of the numeric type.
440.86
Third quartile of standard deviation of attributes of the numeric type.
0.7
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
0.35
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.27
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0
First quartile of means among attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.25
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
0.2
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.74
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.43
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
0.7
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
2
Average number of distinct values among the attributes of the nominal type.
-0.1
First quartile of skewness among attributes of the numeric type.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.25
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
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.42
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.04
Mean skewness among attributes of the numeric type.
0.03
First quartile of standard deviation of attributes of the numeric type.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.43
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
0.25
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
66.67
Percentage of instances belonging to the most frequent class.
121542465.02
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.92
Entropy of the target attribute values.
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
84
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
7.47
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.4
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-0.59
Minimum kurtosis among attributes of the numeric type.
0.07
Second quartile (Median) of means among attributes of the numeric type.
0.25
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.29
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
122.84
Maximum kurtosis among attributes of the numeric type.
-15755088925.87
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

13 tasks

131 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task