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Speech

Speech

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"The speech dataset was also provided by (see citation request) and contains real world data from recorded English language. The normal class contains data from persons having an American accent whereas the outliers are represented from seven other speakers, having a different accent. The feature vector is the i-vector of the speech segment, which is a state-of-the- art feature in speaker recognition. The dataset has 400 dimensions and is thus the task in our evaluation with the largest number of dimensions. It has 3,686 instances including 1.65% anomalies." (cite from Goldstein, Markus, and Seiichi Uchida. "A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data." PloS one 11.4 (2016): e0152173.). This dataset is not the original dataset. The target variable "Target" is relabeled into "Normal" and "Anomaly".

401 features

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Show first 100 features

62 properties

NumberOfInstances
3686
Number of instances (rows) of the dataset.
NumberOfFeatures
401
Number of attributes (columns) of the dataset.
NumberOfClasses
2
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
400
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0.25
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
99.75
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.25
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
0.11
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-0.11
First quartile of means among attributes of the numeric type.
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Quartile1SkewnessOfNumericAtts
-0.04
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.89
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
0.18
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0
Second quartile (Median) of means among attributes of the numeric type.
Quartile2MutualInformation
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Quartile2SkewnessOfNumericAtts
-0
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.91
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
0.26
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
0.12
Third quartile of means among attributes of the numeric type.
Quartile3MutualInformation
Third quartile of mutual information between the nominal attributes and the target attribute.
Quartile3SkewnessOfNumericAtts
0.04
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.92
Third quartile of standard deviation of attributes of the numeric type.
AutoCorrelation
1
Average class difference between consecutive instances.
MeanMeansOfNumericAtts
0.01
Mean of means among attributes of the numeric type.
ClassEntropy
0.12
Entropy of the target attribute values.
Dimensionality
0.11
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.
MajorityClassPercentage
98.35
Percentage of instances belonging to the most frequent class.
MajorityClassSize
3625
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
0.58
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
0.97
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
2
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
0.14
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1.01
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
0.18
Mean kurtosis among attributes of the numeric type.
NumberOfBinaryFeatures
1
Number of binary attributes.
MeanMutualInformation
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
2
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
-0
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.91
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.06
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-0.76
Minimum of means among attributes of the numeric type.
MinMutualInformation
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
-0.19
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.84
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
1.65
Percentage of instances belonging to the least frequent class.
MinorityClassSize
61
Number of instances belonging to the least frequent class.
Show all 62 properties

12 tasks

Supervised Classification on Speech
1599 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: Target
Supervised Classification on Speech
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Target
Clustering on Speech
0 runs
Clustering on Speech
0 runs - estimation_procedure: 50 times Clustering
Clustering on Speech
0 runs - estimation_procedure: 50 times Clustering
Clustering on Speech
0 runs - estimation_procedure: 50 times Clustering
Clustering on Speech
0 runs - estimation_procedure: 50 times Clustering
Clustering on Speech
0 runs - estimation_procedure: 50 times Clustering
Clustering on Speech
0 runs - estimation_procedure: 50 times Clustering
Clustering on Speech
0 runs - estimation_procedure: 50 times Clustering
Clustering on Speech
0 runs - estimation_procedure: 50 times Clustering
Clustering on Speech
0 runs - estimation_procedure: 50 times Clustering
Define a new task