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wall-robot-navigation

wall-robot-navigation

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Author: Ananda Freire, Marcus Veloso and Guilherme Barreto Source: [original](http://www.openml.org/d/1497) - UCI Please cite: * Dataset Title: Wall-Following Robot Navigation Data Data Set (version with 2 Attributes) * Abstract: 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'. * Source: (a) Creators: Ananda Freire, Marcus Veloso and Guilherme Barreto Department of Teleinformatics Engineering Federal University of Ceará Fortaleza, Ceará, Brazil (b) Donors of database: Ananda Freire (anandalf '@' gmail.com) Guilherme Barreto (guilherme '@' deti.ufc.br) * Data Set Information: The provided file contain the 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. 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. If some kind of short-term memory mechanism is provided to the neural classifiers, their performances are improved in general. For example, if past inputs are provided together with current sensor readings, even the Perceptron becomes able to learn the task and command the robot successfully. If a recurrent neural network, such as the Elman network, is used to learn the task, the resulting dynamical classifier is able to learn the task using less hidden neurons than the MLP network. * Attribute Information: Number of Attributes: sensor_readings_2.data: 2 numeric attributes and the class. For Each Attribute: -- File sensor_readings_2.data: 1. SD_front: minimum sensor reading within a 60 degree arc located at the front of the robot - (numeric: real) 2. SD_left: minimum sensor reading within a 60 degree arc located at the left of the robot - (numeric: real) 3. Class: {Move-Forward, Slight-Right-Turn, Sharp-Right-Turn, Slight-Left-Turn} * 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), Valparaíso-Chile, pages 1-6, DOI: 10.1109/LARS.2009.5418323

3 features

Class (target)nominal4 unique values
0 missing
V1numeric1687 unique values
0 missing
V2numeric837 unique values
0 missing
Show all 3 features

107 properties

NumberOfInstances
5456
Number of instances (rows) of the dataset.
NumberOfFeatures
3
Number of attributes (columns) of the dataset.
NumberOfClasses
4
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
2
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.93
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
1
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
CfsSubsetEval_DecisionStumpErrRate
0
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
CfsSubsetEval_DecisionStumpKappa
1
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
CfsSubsetEval_NaiveBayesAUC
1
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
CfsSubsetEval_NaiveBayesErrRate
0
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
CfsSubsetEval_NaiveBayesKappa
1
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
CfsSubsetEval_kNN1NAUC
1
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
CfsSubsetEval_kNN1NErrRate
0
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
CfsSubsetEval_kNN1NKappa
1
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
ClassEntropy
1.71
Entropy of the target attribute values.
DecisionStumpAUC
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.21
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0
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.
J48.00001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
40.41
Percentage of instances belonging to the most frequent class.
MajorityClassSize
2205
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
38.07
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
1.29
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
4
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
5.01
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
0.63
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
20.92
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
0.99
Mean of means among attributes of the numeric type.
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
4
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
3.38
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.48
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
3.77
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.68
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
4
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
1.75
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.34
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
6.01
Percentage of instances belonging to the least frequent class.
MinorityClassSize
328
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.09
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NumberOfBinaryFeatures
0
Number of binary attributes.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
PercentageOfMissingValues
0
Percentage of missing values.
PercentageOfNumericFeatures
66.67
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
33.33
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
3.77
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.68
First quartile of means among attributes of the numeric type.
Quartile1MutualInformation
First quartile of mutual information between the nominal attributes and the target attribute.
Quartile1SkewnessOfNumericAtts
1.75
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.34
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
20.92
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
0.99
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
3.38
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.48
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
38.07
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
1.29
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
5.01
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
0.63
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
StdvNominalAttDistinctValues
0
Standard deviation of the number of distinct values among attributes of the nominal type.
kNN1NAUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.01
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.98
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

14 tasks

Supervised Classification on wall-robot-navigation
78 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on wall-robot-navigation
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on wall-robot-navigation
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
Clustering on wall-robot-navigation
0 runs
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
Clustering on wall-robot-navigation
0 runs - estimation_procedure: 50 times Clustering
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