Data
walking-activity

walking-activity

active ARFF Publicly available Visibility: public Uploaded 26-05-2015 by Rafael G. Mantovani
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Author: P. Casale, O. Pujol, P. Radeva. Source: [UCI](https://archive.ics.uci.edu/ml/datasets/User+Identification+From+Walking+Activity) Please cite: Casale, P. Pujol, O. and Radeva, P. 'Personalization and user verification in wearable systems using biometric walking patterns' Personal and Ubiquitous Computing, 16(5), 563-580, 2012 User Identification From Walking Activity Data Set The dataset collects data from an Android smartphone positioned in the chest pocket. Accelerometer Data are collected from 22 participants walking in the wild over a predefined path. The dataset is intended for Activity Recognition research purposes. It provides challenges for identification and authentication of people using motion patterns. Note: the original per-user datasets were joined into one dataset ### Attribute Information Time-step, x acceleration, y acceleration, z acceleration Target: User ID.

5 features

Class (target)nominal22 unique values
0 missing
V1numeric72087 unique values
0 missing
V2numeric756 unique values
0 missing
V3numeric665 unique values
0 missing
V4numeric733 unique values
0 missing

63 properties

149332
Number of instances (rows) of the dataset.
5
Number of attributes (columns) of the dataset.
22
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.
4
Number of numeric attributes.
1
Number of nominal attributes.
20
Percentage of nominal attributes.
NaN
Third quartile of mutual information between the nominal attributes and the target attribute.
22
The maximum number of distinct values among attributes of the nominal type.
-0.39
Minimum skewness among attributes of the numeric type.
NaN
First quartile of entropy among attributes.
1.19
Third quartile of skewness among attributes of the numeric type.
1.22
Maximum skewness among attributes of the numeric type.
2.77
Minimum standard deviation of attributes of the numeric type.
0.63
First quartile of kurtosis among attributes of the numeric type.
126.16
Third quartile of standard deviation of attributes of the numeric type.
167.16
Maximum standard deviation of attributes of the numeric type.
0.01
Percentage of instances belonging to the least frequent class.
-1.1
First quartile of means among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
-1
Average entropy of the attributes.
911
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
NaN
First quartile of mutual information between the nominal attributes and the target attribute.
1.47
Mean kurtosis among attributes of the numeric type.
-0.37
First quartile of skewness among attributes of the numeric type.
1
Average class difference between consecutive instances.
48.31
Mean of means among attributes of the numeric type.
2.8
First quartile of standard deviation of attributes of the numeric type.
3.99
Entropy of the target attribute values.
NaN
Average mutual information between the nominal attributes and the target attribute.
NaN
Second quartile (Median) of entropy among attributes.
0.15
The predictive accuracy obtained by always predicting the majority class.
NaN
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
1.71
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
22
Average number of distinct values among the attributes of the nominal type.
4.66
Second quartile (Median) of means among attributes of the numeric type.
NaN
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.4
Mean skewness among attributes of the numeric type.
NaN
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
14.73
Percentage of instances belonging to the most frequent class.
43.99
Mean standard deviation of attributes of the numeric type.
0.39
Second quartile (Median) of skewness among attributes of the numeric type.
21991
Number of instances belonging to the most frequent class.
NaN
Minimal entropy among attributes.
0
Percentage of binary attributes.
3.01
Second quartile (Median) of standard deviation of attributes of the numeric type.
NaN
Maximum entropy among attributes.
0.29
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
NaN
Third quartile of entropy among attributes.
2.15
Maximum kurtosis among attributes of the numeric type.
-1.66
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
2.06
Third quartile of kurtosis among attributes of the numeric type.
185.56
Maximum of means among attributes of the numeric type.
NaN
Minimal mutual information between the nominal attributes and the target attribute.
80
Percentage of numeric attributes.
141.36
Third quartile of means among attributes of the numeric type.
NaN
Maximum mutual information between the nominal attributes and the target attribute.
22
The minimal number of distinct values among attributes of the nominal type.

5 tasks

74 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
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