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gas-drift-different-concentrations

gas-drift-different-concentrations

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Author: Alexander Vergara Source: UCI Please cite: A Vergara, S Vembu, T Ayhan, M Ryan, M Homer, R Huerta. "Chemical gas sensor drift compensation using classifier ensembles." Sensors and Actuators B: Chemical 166 (2012): 320-329. I Rodriguez-Lujan, J Fonollosa, A Vergara, M Homer, R Huerta. "On the calibration of sensor arrays for pattern recognition using the minimal number of experiments." Chemometrics and Intelligent Laboratory Systems 130 (2014): 123-134. Source: Creators: Alexander Vergara (vergara '@' ucsd.edu) BioCircutis Institute University of California San Diego San Diego, California, USA Donors of the Dataset: Alexander Vergara (vergara '@' ucsd.edu) Jordi Fonollosa (fonollosa '@'ucsd.edu) Irene Rodriguez-Lujan (irrodriguezlujan '@' ucsd.edu) Ramon Huerta (rhuerta '@' ucsd.edu) Data Set Information: This data set contains 13,910 measurements from 16 chemical sensors exposed to 6 gases at different concentration levels. This dataset is an extension of the Gas Sensor Array Drift Dataset ([Web Link]), providing now the information about the concentration level at which the sensors were exposed for each measurement. The primary purpose of making this dataset freely accessible on-line is to provide an extensive dataset to the sensor and artificial intelligence research communities to develop and test strategies to solve a wide variety of tasks, including sensor drift, classification, regression, among others. The dataset can be used exclusively for research purposes. Commercial purposes are fully excluded. Citation of both Vergara et al. 'Chemical gas sensor drift compensation using classifier ensembles' and Rodriguez-Lujan et al. “On the calibration of sensor arrays for pattern recognition using the minimal number of experiments” is required (see below). The dataset was gathered during the period of January 2008 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. 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. See reference 1 for more details on the experimental setup. The resulting dataset comprises recordings from six distinct pure gaseous substances, namely Ammonia, Acetaldehyde, Acetone, Ethylene, Ethanol, and Toluene, dosed at a wide variety of concentration levels in the intervals (50,1000), (5,500), (12,1000), (10,300), (10,600), and (10,100) ppmv, respectively. Attribute Information: The responses of the said sensors are read in the form of the resistance across the active layer of each sensor; hence, each measurement produced a 16-channel time series, each represented by an aggregate of features reflecting 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 (DR), defined as the maximal resistance change with respect to the baseline and its DR normalized version (DR divided by the acquired value 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. This aggregate of features is a transformation, borrowed from the field of econometrics and originally introduced to the chemo-sensing community by Muezzinoglu et al. (2009), that converts the transient portion of the sensor response into a real scalar by estimating the maximum/minimum value y[k] for the rising/decaying portion of the exponential moving average of the sensor response: y[k] = (1-Alfa) y[k-1]+Alfa(R[k]-R[k-1]) where R[k] is the sensor resistance measured at time k and Alfa is a scalar smoothing parameter between 0 and 1. In particular, three different values for Alfa=0.1, 0.01, 0.001 were set to obtain three different feature values from the rising portion of the sensor response and three additional features with the same Alfa values for the decaying portion of the sensor response, covering thus the entire sensor response dynamics. Thus, each feature vector contains the 8 features extracted from each particular sensor, resulting in a 128-dimensional feature vector (8 features x 16 sensors) containing all the features and organized as follows: DR_1, |DR|_1, EMAi0.001_1, EMAi0.01_1, EMAi0.1_1, EMAd0.001_1, EMAd0.01_1, EMAd0.1_1, DR_2, |DR|_2, EMAi0.001_2, EMAi0.01_2, EMAi0.1_2, EMAd0.001_2, EMAd0.01_2, EMAd0.1_2,..., DR_16, |DR|_16, EMAi0.001_16, EMAi0.01_16, EMAi0.1_16, EMAd0.001_16, EMAd0.01_16, EMAd0.1_16 where: DR_j and |DR|_j are the R and the normalized R features, respectively. EMAi0.001_j, EMAi0.01_j, and EMAi0.1_j, are the emaR of the rising transient portion of the sensor response for Alfa 0.001, 0.01, and 0.1, respectively. EMAd0.001_j, EMAd0.01_j, and EMAd0.1_j, are emaR of the decaying transient portion of the sensor response for Alfa 0.001, 0.01, and 0.1, respectively. The index j=1…16 represents the number of the sensor, forming thus the 128-dimensional feature vector. For processing purposes, the dataset is organized into ten batches, each containing the number of measurements per class and month indicated in the tables below. This reorganization of data was done to ensure having a sufficient and as uniformly distributed as possible number of experiments in each batch. Batch ID Month IDs Batch 1 Months 1 and 2 Batch 2 Months 3, 4, 8, 9 and 10 Batch 3 Months 11, 12, and 13 Batch 4 Months 14 and 15 Batch 5 Month 16 Batch 6 Months 17, 18, 19, and 20 Batch 7 Month 21 Batch 8 Months 22 and 23 Batch 9 Months 24 and 30 Batch 10 Month 36 Batch ID: Ethanol, Ethylene, Ammonia, Acetaldehyde, Acetone, Toluene Batch 1: 83, 30, 70, 98, 90, 74 Batch 2: 100, 109, 532, 334, 164, 5 Batch 3: 216, 240, 275, 490, 365, 0 Batch 4: 12, 30, 12, 43, 64, 0 Batch 5: 20, 46, 63, 40, 28, 0 Batch 6: 110, 29, 606, 574, 514, 467 Batch 7: 360, 744, 630, 662, 649, 568 Batch 8: 40, 33, 143, 30, 30, 18 Batch 9: 100, 75, 78, 55, 61, 101 Batch 10: 600, 600, 600, 600, 600, 600 The dataset is organized in files, each representing a different batch. Within the files, each line represents a measurement. The first character (1-6) codes the analyte, followed by the concentration level: 1: Ethanol; 2: Ethylene; 3: Ammonia; 4: Acetaldehyde; 5: Acetone; 6: Toluene The data format follows the same coding style as in libsvm format x:v, where x stands for the feature number and v for the actual value of the feature. For example, in 1;10.000000 1:15596.162100 2:1.868245 3:2.371604 4:2.803678 5:7.512213 … 128:-2.654529 The number 1 stands for the class number (in this case Ethanol), the gas concentration level was 10ppmv, and the remaining 128 columns list the actual feature values for each measurement recording organized as described above.

130 features

Class (target)nominal6 unique values
0 missing
V1numeric13904 unique values
0 missing
V2numeric13890 unique values
0 missing
V3numeric13904 unique values
0 missing
V4numeric13905 unique values
0 missing
V5numeric13904 unique values
0 missing
V6numeric13897 unique values
0 missing
V7numeric13895 unique values
0 missing
V8numeric13907 unique values
0 missing
V9numeric13897 unique values
0 missing
V10numeric13888 unique values
0 missing
V11numeric13905 unique values
0 missing
V12numeric13909 unique values
0 missing
V13numeric13906 unique values
0 missing
V14numeric13906 unique values
0 missing
V15numeric13902 unique values
0 missing
V16numeric13908 unique values
0 missing
V17numeric13910 unique values
0 missing
V18numeric13892 unique values
0 missing
V19numeric13896 unique values
0 missing
V20numeric13903 unique values
0 missing
V21numeric13909 unique values
0 missing
V22numeric13883 unique values
0 missing
V23numeric13903 unique values
0 missing
V24numeric13899 unique values
0 missing
V25numeric13896 unique values
0 missing
V26numeric13885 unique values
0 missing
V27numeric13891 unique values
0 missing
V28numeric13892 unique values
0 missing
V29numeric13893 unique values
0 missing
V30numeric13872 unique values
0 missing
V31numeric13886 unique values
0 missing
V32numeric13891 unique values
0 missing
V33numeric13904 unique values
0 missing
V34numeric13874 unique values
0 missing
V35numeric13855 unique values
0 missing
V36numeric13894 unique values
0 missing
V37numeric13886 unique values
0 missing
V38numeric13835 unique values
0 missing
V39numeric13869 unique values
0 missing
V40numeric13891 unique values
0 missing
V41numeric13908 unique values
0 missing
V42numeric13877 unique values
0 missing
V43numeric13864 unique values
0 missing
V44numeric13891 unique values
0 missing
V45numeric13894 unique values
0 missing
V46numeric13820 unique values
0 missing
V47numeric13859 unique values
0 missing
V48numeric13882 unique values
0 missing
V49numeric13908 unique values
0 missing
V50numeric13898 unique values
0 missing
V51numeric13906 unique values
0 missing
V52numeric13908 unique values
0 missing
V53numeric13907 unique values
0 missing
V54numeric13893 unique values
0 missing
V55numeric13903 unique values
0 missing
V56numeric13903 unique values
0 missing
V57numeric13909 unique values
0 missing
V58numeric13897 unique values
0 missing
V59numeric13900 unique values
0 missing
V60numeric13905 unique values
0 missing
V61numeric13906 unique values
0 missing
V62numeric13902 unique values
0 missing
V63numeric13901 unique values
0 missing
V64numeric13904 unique values
0 missing
V65numeric13899 unique values
0 missing
V66numeric13889 unique values
0 missing
V67numeric13902 unique values
0 missing
V68numeric13906 unique values
0 missing
V69numeric13907 unique values
0 missing
V70numeric13891 unique values
0 missing
V71numeric13907 unique values
0 missing
V72numeric13906 unique values
0 missing
V73numeric13904 unique values
0 missing
V74numeric13887 unique values
0 missing
V75numeric13904 unique values
0 missing
V76numeric13903 unique values
0 missing
V77numeric13905 unique values
0 missing
V78numeric13897 unique values
0 missing
V79numeric13898 unique values
0 missing
V80numeric13900 unique values
0 missing
V81numeric13908 unique values
0 missing
V82numeric13888 unique values
0 missing
V83numeric13906 unique values
0 missing
V84numeric13906 unique values
0 missing
V85numeric13905 unique values
0 missing
V86numeric13892 unique values
0 missing
V87numeric13899 unique values
0 missing
V88numeric13903 unique values
0 missing
V89numeric13908 unique values
0 missing
V90numeric13900 unique values
0 missing
V91numeric13903 unique values
0 missing
V92numeric13905 unique values
0 missing
V93numeric13903 unique values
0 missing
V94numeric13886 unique values
0 missing
V95numeric13896 unique values
0 missing
V96numeric13902 unique values
0 missing
V97numeric13902 unique values
0 missing
V98numeric13882 unique values
0 missing
V99numeric13872 unique values
0 missing
V100numeric13905 unique values
0 missing
V101numeric13902 unique values
0 missing
V102numeric13854 unique values
0 missing
V103numeric13882 unique values
0 missing
V104numeric13895 unique values
0 missing
V105numeric13910 unique values
0 missing
V106numeric13885 unique values
0 missing
V107numeric13876 unique values
0 missing
V108numeric13894 unique values
0 missing
V109numeric13895 unique values
0 missing
V110numeric13850 unique values
0 missing
V111numeric13875 unique values
0 missing
V112numeric13875 unique values
0 missing
V113numeric13905 unique values
0 missing
V114numeric13898 unique values
0 missing
V115numeric13903 unique values
0 missing
V116numeric13908 unique values
0 missing
V117numeric13906 unique values
0 missing
V118numeric13898 unique values
0 missing
V119numeric13903 unique values
0 missing
V120numeric13907 unique values
0 missing
V121numeric13909 unique values
0 missing
V122numeric13898 unique values
0 missing
V123numeric13903 unique values
0 missing
V124numeric13907 unique values
0 missing
V125numeric13903 unique values
0 missing
V126numeric13898 unique values
0 missing
V127numeric13905 unique values
0 missing
V128numeric13907 unique values
0 missing
V129numeric59 unique values
0 missing

62 properties

13910
Number of instances (rows) of the dataset.
130
Number of attributes (columns) of the dataset.
6
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.
129
Number of numeric attributes.
1
Number of nominal attributes.
15.4
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
6
The minimal number of distinct values among attributes of the nominal type.
99.23
Percentage of numeric attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
6
The maximum number of distinct values among attributes of the nominal type.
-87.65
Minimum skewness among attributes of the numeric type.
0.77
Percentage of nominal attributes.
2.59
Third quartile of skewness among attributes of the numeric type.
117.93
Maximum skewness among attributes of the numeric type.
0.53
Minimum standard deviation of attributes of the numeric type.
First quartile of entropy among attributes.
25.11
Third quartile of standard deviation of attributes of the numeric type.
69844.79
Maximum standard deviation of attributes of the numeric type.
11.8
Percentage of instances belonging to the least frequent class.
4.25
First quartile of kurtosis among attributes of the numeric type.
0
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
1641
Number of instances belonging to the least frequent class.
-4.73
First quartile of means among attributes of the numeric type.
1029.19
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
2771.04
Mean of means among attributes of the numeric type.
-2.27
First quartile of skewness among attributes of the numeric type.
0.59
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
4.36
First quartile of standard deviation of attributes of the numeric type.
2.55
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of entropy among attributes.
0.01
Number of attributes divided by the number of instances.
6
Average number of distinct values among the attributes of the nominal type.
10.15
Second quartile (Median) of kurtosis among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
4.61
Mean skewness among attributes of the numeric type.
5.4
Second quartile (Median) of means among attributes of the numeric type.
21.63
Percentage of instances belonging to the most frequent class.
2709.48
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
3009
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.
1.3
Second quartile (Median) of skewness among attributes of the numeric type.
Maximum entropy among attributes.
-0.07
Minimum kurtosis among attributes of the numeric type.
0
Percentage of binary attributes.
10.05
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
13909.09
Maximum kurtosis among attributes of the numeric type.
-72.75
Minimum of means among attributes of the numeric type.
0
Percentage of instances having missing values.
80.48
Third quartile of kurtosis among attributes of the numeric type.
57340.1
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of missing values.

5 tasks

37 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: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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