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ldpa

ldpa

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Author: Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Localization+Data+for+Person+Activity) Please cite: B. Kaluza, V. Mirchevska, E. Dovgan, M. Lustrek, M. Gams, An Agent-based Approach to Care in Independent Living, International Joint Conference on Ambient Intelligence (AmI-10), Malaga, Spain, In press Dataset Title: Localization Data for Person Activity Data Set Abstract: Data contains recordings of five people performing different activities. Each person wore four sensors (tags) while performing the same scenario five times. Source: - Creators: Mitja Lustrek (mitja.lustrek '@' ijs.si), Bostjan Kaluza (bostjan.kaluza '@' ijs.si), Rok Piltaver (rok.piltaver '@' ijs.si), Jana Krivec (jana.krivec '@' ijs.si), Vedrana Vidulin (vedrana.vidulin '@' ijs.si) - Jozef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenija - Donor: Bozidara Cvetkovic (boza.cvetkovic '@' ijs.si) - Jozef Stefan Institute, Jamova cesta 39, 1000 Ljubljana, Slovenija - Date received: October, 2010 Data Set Information: People used for recording of the data were wearing four tags (ankle left, ankle right, belt and chest). Each instance is a localization data for one of the tags. The tag can be identified by one of the attributes. Attribute Information: Instance example: A01,020-000-033-111,633790226057226795,27.05.2009 14:03:25:723,4.292500972747803,2.0738532543182373,1.36650812625885,walking 1) Sequence Name {A01,A02,A03,A04,A05,B01,B02,B03,B04,B05,C01,C02,C03,C04,C05,D01,D02,D03,D04,D05,E01,E02,E03,E04,E05} (Nominal) - A, B, C, D, E = 5 people 2) Tag identificator {010-000-024-033,020-000-033-111,020-000-032-221,010-000-030-096} (Nominal) - ANKLE_LEFT = 010-000-024-033 - ANKLE_RIGHT = 010-000-030-096 - CHEST = 020-000-033-111 - BELT = 020-000-032-221 3) timestamp (Numeric) all unique 4) date FORMAT = dd.MM.yyyy HH:mm:ss:SSS (Date) 5) x coordinate of the tag (Numeric) 6) y coordinate of the tag (Numeric) 7) z coordinate of the tag (Numeric) 8) activity {walking,falling,'lying down',lying,'sitting down',sitting,'standing up from lying','on all fours','sitting on the ground','standing up from sitting','standing up from sitting on the ground'} (Nominal) Relevant Papers: B. Kaluza, V. Mirchevska, E. Dovgan, M. Lustrek, M. Gams, An Agent-based Approach to Care in Independent Living, International Joint Conference on Ambient Intelligence (AmI-10), Malaga, Spain, In press

8 features

Class (target)nominal11 unique values
0 missing
V1nominal5 unique values
0 missing
V2nominal4 unique values
0 missing
V3numeric164859 unique values
0 missing
V4numeric164834 unique values
0 missing
V5numeric163802 unique values
0 missing
V6numeric163689 unique values
0 missing
V7numeric164482 unique values
0 missing

107 properties

164860
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
11
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.
5
Number of numeric attributes.
3
Number of nominal attributes.
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
82429.68
Maximum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
0
Second quartile (Median) of skewness among attributes of the numeric type.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
0.01
Maximum mutual information between the nominal attributes and the target attribute.
4
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
47463.95
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
407.18
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
11
The maximum number of distinct values among attributes of the nominal type.
-0
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
2.3
Third quartile of entropy among attributes.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0
Maximum skewness among attributes of the numeric type.
47224.78
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-1.2
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
47590.87
Maximum standard deviation of attributes of the numeric type.
0.84
Percentage of instances belonging to the least frequent class.
62.5
Percentage of numeric attributes.
82423.15
Third quartile of means among attributes of the numeric type.
0.99
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
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.15
Average entropy of the attributes.
1381
Number of instances belonging to the least frequent class.
37.5
Percentage of nominal attributes.
0.01
Third quartile of mutual information between the nominal attributes and the target attribute.
0.01
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
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-1.2
Mean kurtosis among attributes of the numeric type.
0.75
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
2
First quartile of entropy among attributes.
0
Third quartile of skewness among attributes of the numeric type.
0.99
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
82188.57
Mean of means among attributes of the numeric type.
0.58
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.2
First quartile of kurtosis among attributes of the numeric type.
47587.41
Third quartile of standard deviation of attributes of the numeric type.
0.99
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
0.17
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.01
Average mutual information between the nominal attributes and the target attribute.
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
81912.93
First quartile of means among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
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
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
321.75
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
0
First quartile of mutual information between the nominal attributes and the target attribute.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.99
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
0.99
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
3.79
Standard deviation of the number of distinct values among attributes of the nominal type.
0.18
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
6.67
Average number of distinct values among the attributes of the nominal type.
-0
First quartile of skewness among attributes of the numeric type.
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.01
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
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.78
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
-0
Mean skewness among attributes of the numeric type.
47228.1
First quartile of standard deviation of attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.99
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
0.26
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
33.05
Percentage of instances belonging to the most frequent class.
47419
Mean standard deviation of attributes of the numeric type.
2.15
Second quartile (Median) of entropy among attributes.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
2.71
Entropy of the target attribute values.
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
54480
Number of instances belonging to the most frequent class.
2
Minimal entropy among attributes.
-1.2
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.74
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.58
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
2.3
Maximum entropy among attributes.
-1.2
Minimum kurtosis among attributes of the numeric type.
82270.72
Second quartile (Median) of means among attributes of the numeric type.
0.2
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.67
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-1.2
Maximum kurtosis among attributes of the numeric type.
81869.99
Minimum of means among attributes of the numeric type.
0.01
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

16 tasks

6 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 4-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
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