Data
Speech

Speech

active ARFF Publicly available Visibility: public Uploaded 22-09-2017 by Minh-Anh Le
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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

Target (target)nominal2 unique values
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0 missing

62 properties

3686
Number of instances (rows) of the dataset.
401
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.
400
Number of numeric attributes.
1
Number of nominal attributes.
0.12
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
99.75
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
-0.19
Minimum skewness among attributes of the numeric type.
0.25
Percentage of nominal attributes.
0.04
Third quartile of skewness among attributes of the numeric type.
0.14
Maximum skewness among attributes of the numeric type.
0.84
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
0.92
Third quartile of standard deviation of attributes of the numeric type.
1.01
Maximum standard deviation of attributes of the numeric type.
1.65
Percentage of instances belonging to the least frequent class.
0.11
First quartile of kurtosis among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
61
Number of instances belonging to the least frequent class.
-0.11
First quartile of means among attributes of the numeric type.
0.18
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.01
Mean of means among attributes of the numeric type.
-0.04
First quartile of skewness among attributes of the numeric type.
1
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
0.89
First quartile of standard deviation of attributes of the numeric type.
0.12
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.11
Number of attributes divided by the number of instances.
2
Average number of distinct values among the attributes of the nominal type.
0.18
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
-0
Mean skewness among attributes of the numeric type.
0
Second quartile (Median) of means among attributes of the numeric type.
98.35
Percentage of instances belonging to the most frequent class.
0.91
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
3625
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.06
Minimum kurtosis among attributes of the numeric type.
0.25
Percentage of binary attributes.
0.91
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
0.58
Maximum kurtosis among attributes of the numeric type.
-0.76
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
0.26
Third quartile of kurtosis among attributes of the numeric type.
0.97
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

4 tasks

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