Data
wall-robot-navigation

wall-robot-navigation

active ARFF Publicly available Visibility: public Uploaded 01-06-2015 by Rafael G. 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 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_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), Valparaíso-Chile, pages 1-6, DOI: 10.1109/LARS.2009.5418323

5 features

Class (target)nominal4 unique values
0 missing
V1numeric1687 unique values
0 missing
V2numeric837 unique values
0 missing
V3numeric1709 unique values
0 missing
V4numeric1779 unique values
0 missing

107 properties

5456
Number of instances (rows) of the dataset.
5
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.
4
Number of numeric attributes.
1
Number of nominal attributes.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
0.49
Minimum kurtosis among attributes of the numeric type.
1.28
Second quartile (Median) of means among attributes of the numeric type.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.21
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
38.07
Maximum kurtosis among attributes of the numeric type.
0.68
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
1.88
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
2.09
Second quartile (Median) of skewness among attributes of the numeric type.
1
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.
0.59
Second quartile (Median) of standard deviation of attributes of the numeric type.
0
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.
0.96
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
1
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
5.01
Maximum skewness among attributes of the numeric type.
0.34
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
30.32
Third quartile of kurtosis among attributes of the numeric type.
0.93
Average class difference between consecutive instances.
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
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.82
Maximum standard deviation of attributes of the numeric type.
6.01
Percentage of instances belonging to the least frequent class.
80
Percentage of numeric attributes.
1.73
Third quartile of means among attributes of the numeric type.
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
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
1
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.
20
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
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
1
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
12.36
Mean kurtosis among attributes of the numeric type.
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
4.37
Third quartile of skewness among attributes of the numeric type.
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
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
1.28
Mean of means among attributes of the numeric type.
0.11
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
1.31
First quartile of kurtosis among attributes of the numeric type.
0.77
Third quartile of standard deviation of attributes of the numeric type.
1
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
Average mutual information between the nominal attributes and the target attribute.
0.84
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.83
First quartile of means among attributes of the numeric type.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
1
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.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
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
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0
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.
1.15
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
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
0.97
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
2.54
Mean skewness among attributes of the numeric type.
0.4
First quartile of standard deviation of attributes of the numeric type.
0
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
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
0.04
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
40.41
Percentage of instances belonging to the most frequent class.
0.59
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.71
Entropy of the target attribute values.
0.94
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
2205
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
5.43
Second quartile (Median) of kurtosis among attributes of the numeric type.

9 tasks

107 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 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: 33% Holdout set - 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
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