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gas-drift

gas-drift

active ARFF Publicly available Visibility: public Uploaded 22-05-2015 by unknown
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  • OpenML100 study_123 study_135 study_14 time_series study_225
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Author: Alexander Vergara Source: [UCI](https://archive.ics.uci.edu/ml/datasets/gas+sensor+array+drift+dataset) - 2012 Please cite: Alexander Vergara, Shankar Vembu, Tuba Ayhan, Margaret A. Ryan, Margie L. Homer, Ramón Huerta. Chemical gas sensor drift compensation using classifier ensembles, Sensors and Actuators B: Chemical (2012) doi: 10.1016/j.snb.2012.01.074. ### Description Gas Sensor Array Drift Dataset Data Set ### Sources ``` (a) Creators: Alexander Vergara (vergara '@' ucsd.edu) BioCircutis Institute University of California San Diego San Diego, California, USA (b) Donors: Alexander Vergara (vergara '@' ucsd.edu) Ramon Huerta (rhuerta '@' ucsd.edu) ``` ### Dataset Information This archive contains 13910 measurements from 16 chemical sensors utilized in simulations for drift compensation in a discrimination task of 6 gases at various levels of concentrations. The goal is to achieve good performance (or as low degradation as possible) over time, as reported in the paper mentioned below in Section 2: Data collection. The primary purpose of providing this dataset is to make it freely accessible online to the chemo-sensor research community and artificial intelligence to develop strategies to cope with sensor/concept drift. The dataset can be used exclusively for research purposes. Commercial purposes are fully excluded. The dataset was gathered within January 2007 to February 2011 (36 months) in a gas delivery platform facility situated at the ChemoSignals Laboratory in the BioCircuits Institute, University of California San Diego. Being completely operated by a fully computerized environment controlled by a LabVIEW's National Instruments software on a PC fitted with the appropriate serial data acquisition boards. The measurement system platform provides versatility for obtaining the desired concentrations of the chemical substances of interest with high accuracy and in a highly reproducible manner, minimizing thereby the common mistakes caused by human intervention and making it possible to exclusively concentrate on the chemical sensors for compensating real drift. The resulting dataset comprises recordings from six distinct pure gaseous substances, namely Ammonia, Acetaldehyde, Acetone, Ethylene, Ethanol, and Toluene, each dosed at a wide variety of concentration values ranging from 5 to 1000 ppmv. An extension of this dataset with the concentration values is available at [Gas Sensor Array Drift Dataset at Different Concentrations Data Set](http://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations). ### Attribute Information The response of the said sensors is read-out in the form of the resistance across the active layer of each sensor. Hence each measurement produced a 16-channel time series, each of which represented by an aggregate of features reflecting all the dynamic processes occurring at the sensor surface in reaction to the chemical substance being evaluated. In particular, two distinct types of features were considered in the creation of this dataset: (i) The so-called steady-state feature (ΔR), defined as the difference of the maximal resistance change and the baseline and its normalized version expressed by the ratio of the maximal resistance and the baseline values when the chemical vapor is present in the test chamber; and (ii) an aggregate of features reflecting the sensor dynamics of the increasing/decaying transient portion of the sensor response during the entire measurement procedure under controlled conditions, namely the exponential moving average (emaα). These aggregate of features is a transform, borrowed from the field of econometrics originally introduced to the chemo-sensing community by Muezzinoglu et al. (2009), that converts the said transient portion into a real scalar, by estimating the maximum value —minimum for the decaying portion of the sensor response— of its exponential moving average (emaα), with an initial condition set to zero and a scalar smoothing parameter of the operator, α, that defines both the quality of the feature and the time of its occurrence along the time series the scalar, set to range between 0 and 1. In particular, three different values for α were set to obtain three different feature values from the pre-recorded rising portion of the sensor response and three additional features with the same α values but for the decaying portion of the sensor response, covering thus the entire sensor response dynamics. For a more detailed analysis and discussion on these features as well as a graphical illustration of them please refer to Section 2.3 and Figure 2, respectively of the annotated manuscript. Once the abovementioned features are calculated, one is to form a feature vector containing the 8 features extracted from each particular sensor multiplied by the 16 sensors considered here. In the end, there is a resulting 128-dimensional feature vector containing all the features indicated above. There are six possible classes: ``` 1: Ethanol 2: Ethylene 3: Ammonia 4: Acetaldehyde 5: Acetone 6: Toluene ``` ### Relevant Papers Alexander Vergara, Shankar Vembu, Tuba Ayhan, Margaret A. Ryan, Margie L. Homer and Ramón Huerta, Chemical gas sensor drift compensation using classifier ensembles, Sensors and Actuators B: Chemical (2012) doi: 10.1016/j.snb.2012.01.074.

129 features

Class (target)nominal6 unique values
0 missing
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0 missing
V89numeric13908 unique values
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0 missing
V101numeric13902 unique values
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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0 missing
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V128numeric13907 unique values
0 missing
Show first 100 features

107 properties

NumberOfInstances
13910
Number of instances (rows) of the dataset.
NumberOfFeatures
129
Number of attributes (columns) of the dataset.
NumberOfClasses
6
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
128
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.59
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.97
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
0.05
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
0.94
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
0.97
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
0.05
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
0.94
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
0.97
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
0.05
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
0.94
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
2.55
Entropy of the target attribute values.
DecisionStumpAUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.61
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.01
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
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.03
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.96
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
21.63
Percentage of instances belonging to the most frequent class.
MajorityClassSize
3009
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
13909.09
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
57340.1
Maximum of means among attributes of the numeric type.
MaxMutualInformation
Maximum mutual information between the nominal attributes and the target attribute.
MaxNominalAttDistinctValues
6
The maximum number of distinct values among attributes of the nominal type.
MaxSkewnessOfNumericAtts
117.93
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
69844.79
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
1037.15
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
2791.46
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
6
Average number of distinct values among the attributes of the nominal type.
MeanSkewnessOfNumericAtts
4.62
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
2729.31
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.07
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-72.75
Minimum of means among attributes of the numeric type.
MinMutualInformation
Minimal mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
6
The minimal number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-87.65
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0.53
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
11.8
Percentage of instances belonging to the least frequent class.
MinorityClassSize
1641
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.84
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.43
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.49
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
99.22
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
0.78
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
4.23
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
-4.77
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
-2.29
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
4.36
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
10.32
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
5.37
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
1.3
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
9.63
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
80.89
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
15.19
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
2.54
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
24.79
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.99
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.95
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.04
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.95
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
1
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
0.99
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

44 tasks

Supervised Classification on gas-drift
10683 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on gas-drift
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
Supervised Classification on gas-drift
1 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
Supervised Classification on gas-drift
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
Supervised Classification on gas-drift
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
Supervised Classification on gas-drift
0 runs - estimation_procedure: Test on Training Data - target_feature: Class
Supervised Classification on gas-drift
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
Learning Curve on gas-drift
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature:
Clustering on gas-drift
0 runs - target_feature: Class
Clustering on gas-drift
0 runs
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Clustering on gas-drift
0 runs - estimation_procedure: 50 times Clustering
Subgroup Discovery on gas-drift
1305 runs - target_feature: Class
Subgroup Discovery on gas-drift
1303 runs - target_feature: Class
Subgroup Discovery on gas-drift
1302 runs - target_feature: Class
Subgroup Discovery on gas-drift
1301 runs - target_feature: Class
Subgroup Discovery on gas-drift
1300 runs - target_feature: Class
Subgroup Discovery on gas-drift
1296 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
Subgroup Discovery on gas-drift
0 runs - target_feature: Class
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