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wall-robot-navigation

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael Gomes Mantovani
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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 4 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 CearÃ¡ Fortaleza, CearÃ¡, 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_24.data: 24 numeric attributes and the class. For Each Attribute: -- File sensor_readings_4.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. SD_right: minimum sensor reading within a 60 degree arc located at the right of the robot - (numeric: real) 4. SD_back: minimum sensor reading within a 60 degree arc located at the back of the robot - (numeric: real) 5. 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), ValparaÃso-Chile, pages 1-6, DOI: 10.1109/LARS.2009.5418323

5 features

Class (target)	nominal	4 unique values 0 missing
V1	numeric	1687 unique values 0 missing
V2	numeric	837 unique values 0 missing
V3	numeric	1709 unique values 0 missing
V4	numeric	1779 unique values 0 missing

Show all 5 features

107 properties

NumberOfInstances

5456

Number of instances (rows) of the dataset.

NumberOfFeatures

Number of attributes (columns) of the dataset.

NumberOfClasses

Number of distinct values of the target attribute (if it is nominal).

NumberOfMissingValues

Number of missing values in the dataset.

NumberOfInstancesWithMissingValues

Number of instances with at least one value missing.

NumberOfNumericFeatures

Number of numeric attributes.

NumberOfSymbolicFeatures

Number of nominal attributes.

REPTreeDepth1Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1

CfsSubsetEval_kNN1NAUC

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

StdvNominalAttDistinctValues

Standard deviation of the number of distinct values among attributes of the nominal type.

J48.001.ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001

MeanNominalAttDistinctValues

Average number of distinct values among the attributes of the nominal type.

Quartile1SkewnessOfNumericAtts

1.15

First quartile of skewness among attributes of the numeric type.

REPTreeDepth2AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2

CfsSubsetEval_kNN1NErrRate

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

kNN1NAUC

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk

J48.001.Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001

MeanSkewnessOfNumericAtts

2.54

Mean skewness among attributes of the numeric type.

Quartile1StdDevOfNumericAtts

0.4

First quartile of standard deviation of attributes of the numeric type.

REPTreeDepth2ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2

CfsSubsetEval_kNN1NKappa

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

kNN1NErrRate

0.04

Error rate achieved by the landmarker weka.classifiers.lazy.IBk

MajorityClassPercentage

40.41

Percentage of instances belonging to the most frequent class.

MeanStdDevOfNumericAtts

0.59

Mean standard deviation of attributes of the numeric type.

Quartile2AttributeEntropy

Second quartile (Median) of entropy among attributes.

REPTreeDepth2Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2

ClassEntropy

1.71

Entropy of the target attribute values.

kNN1NKappa

0.94

Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

MajorityClassSize

2205

Number of instances belonging to the most frequent class.

MinAttributeEntropy

Minimal entropy among attributes.

Quartile2KurtosisOfNumericAtts

5.43

Second quartile (Median) of kurtosis among attributes of the numeric type.

REPTreeDepth3AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3

DecisionStumpAUC

0.87

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump

MaxAttributeEntropy

Maximum entropy among attributes.

MinKurtosisOfNumericAtts

0.49

Minimum kurtosis among attributes of the numeric type.

Quartile2MeansOfNumericAtts

1.28

Second quartile (Median) of means among attributes of the numeric type.

REPTreeDepth3ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3

DecisionStumpErrRate

0.21

Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump

MaxKurtosisOfNumericAtts

38.07

Maximum kurtosis among attributes of the numeric type.

MinMeansOfNumericAtts

0.68

Minimum of means among attributes of the numeric type.

Quartile2MutualInformation

Second quartile (Median) of mutual information between the nominal attributes and the target attribute.

Quartile2SkewnessOfNumericAtts

2.09

Second quartile (Median) of skewness among attributes of the numeric type.

REPTreeDepth3Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3

DecisionStumpKappa

0.65

Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump

MaxMeansOfNumericAtts

1.88

Maximum of means among attributes of the numeric type.

MinMutualInformation

Minimal mutual information between the nominal attributes and the target attribute.

Quartile2StdDevOfNumericAtts

0.59

Second quartile (Median) of standard deviation of attributes of the numeric type.

RandomTreeDepth1AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

Dimensionality

Number of attributes divided by the number of instances.

MaxMutualInformation

Maximum mutual information between the nominal attributes and the target attribute.

MinNominalAttDistinctValues

The minimal number of distinct values among attributes of the nominal type.

PercentageOfBinaryFeatures

Percentage of binary attributes.

Quartile3AttributeEntropy

Third quartile of entropy among attributes.

RandomTreeDepth1ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

EquivalentNumberOfAtts

Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.

MaxNominalAttDistinctValues

The maximum number of distinct values among attributes of the nominal type.

MinSkewnessOfNumericAtts

0.96

Minimum skewness among attributes of the numeric type.

PercentageOfInstancesWithMissingValues

Percentage of instances having missing values.

Quartile3KurtosisOfNumericAtts

30.32

Third quartile of kurtosis among attributes of the numeric type.

AutoCorrelation

0.93

Average class difference between consecutive instances.

RandomTreeDepth1Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1

J48.00001.AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001

MaxSkewnessOfNumericAtts

5.01

Maximum skewness among attributes of the numeric type.

MinStdDevOfNumericAtts

0.34

Minimum standard deviation of attributes of the numeric type.

PercentageOfMissingValues

Percentage of missing values.

Quartile3MeansOfNumericAtts

1.73

Third quartile of means among attributes of the numeric type.

CfsSubsetEval_DecisionStumpAUC

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

RandomTreeDepth2AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

J48.00001.ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001

MaxStdDevOfNumericAtts

0.82

Maximum standard deviation of attributes of the numeric type.

MinorityClassPercentage

6.01

Percentage of instances belonging to the least frequent class.

PercentageOfNumericFeatures

Percentage of numeric attributes.

Quartile3MutualInformation

Third quartile of mutual information between the nominal attributes and the target attribute.

CfsSubsetEval_DecisionStumpErrRate

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

RandomTreeDepth2ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

J48.00001.Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001

MeanAttributeEntropy

Average entropy of the attributes.

MinorityClassSize

328

Number of instances belonging to the least frequent class.

PercentageOfSymbolicFeatures

Percentage of nominal attributes.

Quartile3SkewnessOfNumericAtts

4.37

Third quartile of skewness among attributes of the numeric type.

CfsSubsetEval_DecisionStumpKappa

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

RandomTreeDepth2Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2

J48.0001.AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001

MeanKurtosisOfNumericAtts

12.36

Mean kurtosis among attributes of the numeric type.

NaiveBayesAUC

0.97

Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Quartile1AttributeEntropy

First quartile of entropy among attributes.

Quartile3StdDevOfNumericAtts

0.77

Third quartile of standard deviation of attributes of the numeric type.

CfsSubsetEval_NaiveBayesAUC

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

RandomTreeDepth3AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

J48.0001.ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001

MeanMeansOfNumericAtts

1.28

Mean of means among attributes of the numeric type.

NaiveBayesErrRate

0.11

Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Quartile1KurtosisOfNumericAtts

1.31

First quartile of kurtosis among attributes of the numeric type.

REPTreeDepth1AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1

CfsSubsetEval_NaiveBayesErrRate

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

RandomTreeDepth3ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

J48.0001.Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

MeanMutualInformation

Average mutual information between the nominal attributes and the target attribute.

NaiveBayesKappa

0.84

Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes

Quartile1MeansOfNumericAtts

0.83

First quartile of means among attributes of the numeric type.

REPTreeDepth1ErrRate

Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1

CfsSubsetEval_NaiveBayesKappa

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

RandomTreeDepth3Kappa

Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3

J48.001.AUC

Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001

MeanNoiseToSignalRatio

An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.

NumberOfBinaryFeatures

Number of binary attributes.

Quartile1MutualInformation

First quartile of mutual information between the nominal attributes and the target attribute.

Show all 107 properties