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wall-robot-navigation

wall-robot-navigation

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  • Chemistry Life Science OpenML-CC18 OpenML100 study_123 study_14 study_34 study_52 study_7 study_98 study_99 study_225 study_236 study_293 study_253 study_275
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Author: Ananda Freire, Marcus Veloso and Guilherme Barreto Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Wall-Following+Robot+Navigation+Data) - 2010 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) Wall-Following Robot Navigation Data Data Set The data were collected as the SCITOS G5 robot navigates through the room following the wall in a clockwise direction, for 4 rounds, using 24 ultrasound sensors arranged circularly around its 'waist'. The data consists of raw values of the measurements of all 24 ultrasound sensors and the corresponding class label. Sensor readings are sampled at a rate of 9 samples per second. The class labels are: 1. Move-Forward, 2. Slight-Right-Turn, 3. Sharp-Right-Turn, 4. Slight-Left-Turn It is worth mentioning that the 24 ultrasound readings and the simplified distances were collected at the same time step, so each file has the same number of rows (one for each sampling time step). The wall-following task and data gathering were designed to test the hypothesis that this apparently simple navigation task is indeed a non-linearly separable classification task. Thus, linear classifiers, such as the Perceptron network, are not able to learn the task and command the robot around the room without collisions. Nonlinear neural classifiers, such as the MLP network, are able to learn the task and command the robot successfully without collisions. ### Attribute Information: 1. US1: ultrasound sensor at the front of the robot (reference angle: 180°) 2. US2: ultrasound reading (reference angle: -165°) 3. US3: ultrasound reading (reference angle: -150°) 4. US4: ultrasound reading (reference angle: -135°) 5. US5: ultrasound reading (reference angle: -120°) 6. US6: ultrasound reading (reference angle: -105°) 7. US7: ultrasound reading (reference angle: -90°) 8. US8: ultrasound reading (reference angle: -75°) 9. US9: ultrasound reading (reference angle: -60°) 10. US10: ultrasound reading (reference angle: -45°) 11. US11: ultrasound reading (reference angle: -30°) 12. US12: ultrasound reading (reference angle: -15°) 13. US13: reading of ultrasound sensor situated at the back of the robot (reference angle: 0°) 14. US14: ultrasound reading (reference angle: 15°) 15. US15: ultrasound reading (reference angle: 30°) 16. US16: ultrasound reading (reference angle: 45°) 17. US17: ultrasound reading (reference angle: 60°) 18. US18: ultrasound reading (reference angle: 75°) 19. US19: ultrasound reading (reference angle: 90°) 20. US20: ultrasound reading (reference angle: 105°) 21. US21: ultrasound reading (reference angle: 120°) 22. US22: ultrasound reading (reference angle: 135°) 23. US23: ultrasound reading (reference angle: 150°) 24. US24: ultrasound reading (reference angle: 165°) ### Relevant Papers Ananda L. Freire, Guilherme A. Barreto, Marcus Veloso and Antonio T. Varela (2009), 'Short-Term Memory Mechanisms in Neural Network Learning of Robot Navigation Tasks: A Case Study'. Proceedings of the 6th Latin American Robotics Symposium (LARS'2009), pages 1-6

25 features

Class (target)nominal4 unique values
0 missing
V1numeric1977 unique values
0 missing
V2numeric2034 unique values
0 missing
V3numeric1786 unique values
0 missing
V4numeric1767 unique values
0 missing
V5numeric1822 unique values
0 missing
V6numeric1828 unique values
0 missing
V7numeric1530 unique values
0 missing
V8numeric2068 unique values
0 missing
V9numeric1870 unique values
0 missing
V10numeric2003 unique values
0 missing
V11numeric1873 unique values
0 missing
V12numeric1797 unique values
0 missing
V13numeric1570 unique values
0 missing
V14numeric1487 unique values
0 missing
V15numeric1465 unique values
0 missing
V16numeric1295 unique values
0 missing
V17numeric1083 unique values
0 missing
V18numeric971 unique values
0 missing
V19numeric1042 unique values
0 missing
V20numeric1136 unique values
0 missing
V21numeric1355 unique values
0 missing
V22numeric1736 unique values
0 missing
V23numeric1758 unique values
0 missing
V24numeric1856 unique values
0 missing

107 properties

5456
Number of instances (rows) of the dataset.
25
Number of attributes (columns) of the dataset.
4
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.
24
Number of numeric attributes.
1
Number of nominal attributes.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
4
Average number of distinct values among the attributes of the nominal type.
0.72
First quartile of skewness among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.9
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
1.52
Mean skewness among attributes of the numeric type.
1.12
First quartile of standard deviation of attributes of the numeric type.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.98
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
0.14
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
40.41
Percentage of instances belonging to the most frequent class.
1.25
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.71
Entropy of the target attribute values.
0.79
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
2205
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.08
Second quartile (Median) of kurtosis among attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.78
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
-1.6
Minimum kurtosis among attributes of the numeric type.
2.16
Second quartile (Median) of means among attributes of the numeric type.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.3
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
14.35
Maximum kurtosis among attributes of the numeric type.
0.91
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
1.18
Second quartile (Median) of skewness among attributes of the numeric type.
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.5
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
3.35
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
1.29
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.98
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.
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.
Third quartile of entropy among attributes.
0.03
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.
4
The maximum number of distinct values among attributes of the nominal type.
0.02
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
5.98
Third quartile of kurtosis among attributes of the numeric type.
0.93
Average class difference between consecutive instances.
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
3.83
Maximum skewness among attributes of the numeric type.
0.8
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
2.73
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.72
Maximum standard deviation of attributes of the numeric type.
6.01
Percentage of instances belonging to the least frequent class.
96
Percentage of numeric attributes.
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
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
328
Number of instances belonging to the least frequent class.
4
Percentage of nominal attributes.
2.54
Third quartile of skewness among attributes of the numeric type.
0.98
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
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.37
Mean kurtosis among attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
1.4
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.01
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
2.05
Mean of means among attributes of the numeric type.
0.47
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.97
First quartile of kurtosis among attributes of the numeric type.
1
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
0.03
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.27
First quartile of means among attributes of the numeric type.
0.01
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.98
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
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
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.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.

36 tasks

20384 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
32 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10 times 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
43 runs - estimation_procedure: 10-fold Learning Curve - 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 - 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
1303 runs - target_feature: Class
1302 runs - target_feature: Class
1301 runs - target_feature: Class
1298 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
0 runs - target_feature: Class
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