Data
spectrometer

spectrometer

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  • Astronomy Machine Learning study_1 study_7 uci
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Author: Source: Unknown - 1988 Please cite: 1. Title: Part of the IRAS Low Resolution Spectrometer Database 2. Sources: (a) Originator: Infra-Red Astronomy Satellite Project Database (b) Donor: John Stutz (c) Date: March 1988 (approximately) 3. Past Usage: unknown -- A NASA-Ames research group concerned with unsupervised learning tasks may have used this database during their empirical studies of their algorithm/system (AUTOCLASS II). See the 1988 Machine Learning Conference Proceedings, 54-64, for a description of their algorithm. 4. Relevant Information: (from John Stutz) The Infra-Red Astronomy Satellite (IRAS) was the first attempt to map the full sky at infra-red wavelengths. This could not be done from ground observatories because large portions of the infra-red spectrum is absorbed by the atmosphere. The primary observing program was the full high resolution sky mapping performed by scanning at 4 frequencies. The Low Resolution Observation (IRAS-LRS) program observed high intensity sources over two continuous spectral bands. This database derives from a subset of the higher quality LRS observations taken between 12h and 24h right ascension. This database contains 531 high quality spectra derived from the IRAS-LRS database. The original data contained 100 spectral measurements in each of two overlapping bands. Of these, 44 blue band and 49 red band channels contain usable flux measurements. Only these are included here. The original spectral intensities values are compressed to 4-digits, and each spectrum includes 5 rescaling parameters. We have used the LRS specified algorithm to rescale these to units of spectral intensity (Janskys). Total intensity differences have been eliminated by normalizing each spectrum to a mean value of 5000. This database was originally obtained for use in development and testing of our AutoClass system for Bayesian classification. We have not retained any results from this development, having concentrated our efforts of a 5425 element version of the same data. Our classifications were based upon simultaneous modeling of all 93 spectral intensities. With the larger database we were able to find classes that correspond well with known spectral types associated with particular stellar types. We also found classes that match with the spectra expected of certain stellar processes under investigation by Ames astronomers. These classes have considerably enlarged the set of stars being investigated by those researchers. Original Data The original fortran data file is given in spectra-2.data. The file spectra-2.head contains information about the .data file contents and how to rescale the compressed spectral intensities. 5. Number of Instances: 531 6. Number of Attributes: 103 (including the 10-attribute "header") 7. Attribute Information: 1. LRS-name: (Suspected format: 5 digits, "+" or "-", 4 digits) 2. LRS-class: integer - The LRS-class values range from 0 - 99 with the 10's digit giving the basic class and the 1's digit giving the subclass. These classes are based on features (peaks, valleys, and trends) of the spectral curves. 3. ID-type: integer 4. Right-Ascension: float - Astronomical longitude. 1h = 15deg 5. Declination: float - Astronomical lattitude. -90 <= Dec <= 90 6. Scale Factor: float - Proportional to source strength 7. Blue base 1: integer - linear rescaling coefficient 8. Blue base 2: integer - linear rescaling coefficient 9. Red base 1: integer - linear rescaling coefficient 10. Red base 2: integer - linear rescaling coefficient 11-54: fluxes from the following 44 blue-band channel wavelengths: (all given as floating point numerals) 55-103: fluxes from the following 49 red-band channel wavelengths: (all given as floating point numerals) UCI: http://archive.ics.uci.edu/ml/datasets/Low+Resolution+Spectrometer

102 features

LRS-class (target)nominal48 unique values
0 missing
LRS-name (ignore)nominal531 unique values
0 missing
ID-typenominal4 unique values
0 missing
Right-Ascensionnumeric518 unique values
0 missing
Declinationnumeric530 unique values
0 missing
Scale_Factornumeric397 unique values
0 missing
Blue_base_1numeric461 unique values
0 missing
Blue_base_2numeric468 unique values
0 missing
Red_base_1numeric426 unique values
0 missing
Red_base_2numeric417 unique values
0 missing
blue-band-flux_1numeric531 unique values
0 missing
blue-band-flux_2numeric531 unique values
0 missing
blue-band-flux_3numeric531 unique values
0 missing
blue-band-flux_4numeric531 unique values
0 missing
blue-band-flux_5numeric531 unique values
0 missing
blue-band-flux_6numeric531 unique values
0 missing
blue-band-flux_7numeric531 unique values
0 missing
blue-band-flux_8numeric531 unique values
0 missing
blue-band-flux_9numeric531 unique values
0 missing
blue-band-flux_10numeric531 unique values
0 missing
blue-band-flux_11numeric531 unique values
0 missing
blue-band-flux_12numeric531 unique values
0 missing
blue-band-flux_13numeric531 unique values
0 missing
blue-band-flux_14numeric531 unique values
0 missing
blue-band-flux_15numeric531 unique values
0 missing
blue-band-flux_16numeric530 unique values
0 missing
blue-band-flux_17numeric531 unique values
0 missing
blue-band-flux_18numeric531 unique values
0 missing
blue-band-flux_19numeric531 unique values
0 missing
blue-band-flux_20numeric531 unique values
0 missing
blue-band-flux_21numeric531 unique values
0 missing
blue-band-flux_22numeric531 unique values
0 missing
blue-band-flux_23numeric531 unique values
0 missing
blue-band-flux_24numeric531 unique values
0 missing
blue-band-flux_25numeric531 unique values
0 missing
blue-band-flux_26numeric531 unique values
0 missing
blue-band-flux_27numeric531 unique values
0 missing
blue-band-flux_28numeric531 unique values
0 missing
blue-band-flux_29numeric531 unique values
0 missing
blue-band-flux_30numeric531 unique values
0 missing
blue-band-flux_31numeric531 unique values
0 missing
blue-band-flux_32numeric531 unique values
0 missing
blue-band-flux_33numeric531 unique values
0 missing
blue-band-flux_34numeric531 unique values
0 missing
blue-band-flux_35numeric531 unique values
0 missing
blue-band-flux_36numeric531 unique values
0 missing
blue-band-flux_37numeric531 unique values
0 missing
blue-band-flux_38numeric531 unique values
0 missing
blue-band-flux_39numeric531 unique values
0 missing
blue-band-flux_40numeric531 unique values
0 missing
blue-band-flux_41numeric531 unique values
0 missing
blue-band-flux_42numeric531 unique values
0 missing
blue-band-flux_43numeric531 unique values
0 missing
blue-band-flux_44numeric531 unique values
0 missing
red-band-flux_1numeric531 unique values
0 missing
red-band-flux_2numeric531 unique values
0 missing
red-band-flux_3numeric531 unique values
0 missing
red-band-flux_4numeric531 unique values
0 missing
red-band-flux_5numeric531 unique values
0 missing
red-band-flux_6numeric531 unique values
0 missing
red-band-flux_7numeric531 unique values
0 missing
red-band-flux_8numeric531 unique values
0 missing
red-band-flux_9numeric531 unique values
0 missing
red-band-flux_10numeric531 unique values
0 missing
red-band-flux_11numeric531 unique values
0 missing
red-band-flux_12numeric531 unique values
0 missing
red-band-flux_13numeric531 unique values
0 missing
red-band-flux_14numeric531 unique values
0 missing
red-band-flux_15numeric531 unique values
0 missing
red-band-flux_16numeric531 unique values
0 missing
red-band-flux_17numeric531 unique values
0 missing
red-band-flux_18numeric531 unique values
0 missing
red-band-flux_19numeric531 unique values
0 missing
red-band-flux_20numeric531 unique values
0 missing
red-band-flux_21numeric531 unique values
0 missing
red-band-flux_22numeric531 unique values
0 missing
red-band-flux_23numeric531 unique values
0 missing
red-band-flux_24numeric531 unique values
0 missing
red-band-flux_25numeric531 unique values
0 missing
red-band-flux_26numeric531 unique values
0 missing
red-band-flux_27numeric531 unique values
0 missing
red-band-flux_28numeric531 unique values
0 missing
red-band-flux_29numeric531 unique values
0 missing
red-band-flux_30numeric531 unique values
0 missing
red-band-flux_31numeric531 unique values
0 missing
red-band-flux_32numeric531 unique values
0 missing
red-band-flux_33numeric531 unique values
0 missing
red-band-flux_34numeric531 unique values
0 missing
red-band-flux_35numeric531 unique values
0 missing
red-band-flux_36numeric531 unique values
0 missing
red-band-flux_37numeric531 unique values
0 missing
red-band-flux_38numeric531 unique values
0 missing
red-band-flux_39numeric531 unique values
0 missing
red-band-flux_40numeric531 unique values
0 missing
red-band-flux_41numeric531 unique values
0 missing
red-band-flux_42numeric531 unique values
0 missing
red-band-flux_43numeric531 unique values
0 missing
red-band-flux_44numeric531 unique values
0 missing
red-band-flux_45numeric531 unique values
0 missing
red-band-flux_46numeric531 unique values
0 missing
red-band-flux_47numeric531 unique values
0 missing
red-band-flux_48numeric531 unique values
0 missing
red-band-flux_49numeric531 unique values
0 missing
Show first 100 features

107 properties

NumberOfInstances
531
Number of instances (rows) of the dataset.
NumberOfFeatures
102
Number of attributes (columns) of the dataset.
NumberOfClasses
48
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
100
Number of numeric attributes.
NumberOfSymbolicFeatures
2
Number of nominal attributes.
Quartile2SkewnessOfNumericAtts
1.44
Second quartile (Median) of skewness among attributes of the numeric type.
REPTreeDepth3Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpKappa
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxMeansOfNumericAtts
12541.21
Maximum of means among attributes of the numeric type.
MinMutualInformation
0.43
Minimal mutual information between the nominal attributes and the target attribute.
PercentageOfBinaryFeatures
0
Percentage of binary attributes.
Quartile2StdDevOfNumericAtts
1033.12
Second quartile (Median) of standard deviation of attributes of the numeric type.
RandomTreeDepth1AUC
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Dimensionality
0.19
Number of attributes divided by the number of instances.
MaxMutualInformation
4.69
Maximum mutual information between the nominal attributes and the target attribute.
MinNominalAttDistinctValues
4
The minimal number of distinct values among attributes of the nominal type.
PercentageOfInstancesWithMissingValues
0
Percentage of instances having missing values.
Quartile3AttributeEntropy
9.05
Third quartile of entropy among attributes.
RandomTreeDepth1ErrRate
0.72
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
EquivalentNumberOfAtts
1.83
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
MaxNominalAttDistinctValues
531
The maximum number of distinct values among attributes of the nominal type.
MinSkewnessOfNumericAtts
-1.77
Minimum skewness among attributes of the numeric type.
PercentageOfMissingValues
0
Percentage of missing values.
Quartile3KurtosisOfNumericAtts
8.51
Third quartile of kurtosis among attributes of the numeric type.
AutoCorrelation
0.06
Average class difference between consecutive instances.
RandomTreeDepth1Kappa
0.23
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
J48.00001.AUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxSkewnessOfNumericAtts
15.42
Maximum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
PercentageOfNumericFeatures
98.04
Percentage of numeric attributes.
Quartile3MeansOfNumericAtts
7359.54
Third quartile of means among attributes of the numeric type.
CfsSubsetEval_DecisionStumpAUC
0.76
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.ErrRate
0.61
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MaxStdDevOfNumericAtts
5503.17
Maximum standard deviation of attributes of the numeric type.
MinorityClassPercentage
0.19
Percentage of instances belonging to the least frequent class.
PercentageOfSymbolicFeatures
1.96
Percentage of nominal attributes.
Quartile3MutualInformation
4.69
Third quartile of mutual information between the nominal attributes and the target attribute.
CfsSubsetEval_DecisionStumpErrRate
0.56
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
0.72
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.00001.Kappa
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
MeanAttributeEntropy
5.44
Average entropy of the attributes.
MinorityClassSize
1
Number of instances belonging to the least frequent class.
Quartile1AttributeEntropy
1.83
First quartile of entropy among attributes.
Quartile3SkewnessOfNumericAtts
2.25
Third quartile of skewness among attributes of the numeric type.
CfsSubsetEval_DecisionStumpKappa
0.41
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
0.23
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
J48.0001.AUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanKurtosisOfNumericAtts
8.73
Mean kurtosis among attributes of the numeric type.
NaiveBayesAUC
0.82
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1KurtosisOfNumericAtts
2.46
First quartile of kurtosis among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1396.74
Third quartile of standard deviation of attributes of the numeric type.
CfsSubsetEval_NaiveBayesAUC
0.76
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
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.ErrRate
0.61
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMeansOfNumericAtts
4677.61
Mean of means among attributes of the numeric type.
NaiveBayesErrRate
0.73
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MeansOfNumericAtts
1834.51
First quartile of means among attributes of the numeric type.
REPTreeDepth1AUC
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesErrRate
0.56
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
0.72
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.0001.Kappa
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
MeanMutualInformation
2.56
Average mutual information between the nominal attributes and the target attribute.
NaiveBayesKappa
0.24
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
Quartile1MutualInformation
0.43
First quartile of mutual information between the nominal attributes and the target attribute.
REPTreeDepth1ErrRate
0.9
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_NaiveBayesKappa
0.41
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
0.23
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
J48.001.AUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNoiseToSignalRatio
1.13
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
NumberOfBinaryFeatures
0
Number of binary attributes.
Quartile1SkewnessOfNumericAtts
-1.05
First quartile of skewness among attributes of the numeric type.
REPTreeDepth1Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
CfsSubsetEval_kNN1NAUC
0.76
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
292.39
Standard deviation of the number of distinct values among attributes of the nominal type.
J48.001.ErrRate
0.61
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanNominalAttDistinctValues
194.33
Average number of distinct values among the attributes of the nominal type.
Quartile1StdDevOfNumericAtts
820.13
First quartile of standard deviation of attributes of the numeric type.
REPTreeDepth2AUC
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NErrRate
0.56
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
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
J48.001.Kappa
0.36
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MeanSkewnessOfNumericAtts
1.12
Mean skewness among attributes of the numeric type.
Quartile2AttributeEntropy
5.44
Second quartile (Median) of entropy among attributes.
REPTreeDepth2ErrRate
0.9
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
CfsSubsetEval_kNN1NKappa
0.41
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
0.63
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassPercentage
10.36
Percentage of instances belonging to the most frequent class.
MeanStdDevOfNumericAtts
1314.82
Mean standard deviation of attributes of the numeric type.
Quartile2KurtosisOfNumericAtts
4.24
Second quartile (Median) of kurtosis among attributes of the numeric type.
REPTreeDepth2Kappa
0
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
ClassEntropy
4.69
Entropy of the target attribute values.
kNN1NKappa
0.33
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
MajorityClassSize
55
Number of instances belonging to the most frequent class.
MinAttributeEntropy
1.83
Minimal entropy among attributes.
Quartile2MeansOfNumericAtts
3765.59
Second quartile (Median) of means among attributes of the numeric type.
REPTreeDepth3AUC
0.48
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpAUC
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxAttributeEntropy
9.05
Maximum entropy among attributes.
MinKurtosisOfNumericAtts
-1.18
Minimum kurtosis among attributes of the numeric type.
Quartile2MutualInformation
2.56
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
REPTreeDepth3ErrRate
0.9
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
DecisionStumpErrRate
0.81
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
MaxKurtosisOfNumericAtts
298.23
Maximum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
-9.85
Minimum of means among attributes of the numeric type.
Show all 107 properties

15 tasks

Supervised Classification on spectrometer
1191 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: area_under_roc_curve - target_feature: LRS-class
Supervised Classification on spectrometer
52 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: LRS-class
Supervised Classification on spectrometer
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: LRS-class
Supervised Data Stream Classification on spectrometer
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: LRS-class
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Clustering on spectrometer
0 runs
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Clustering on spectrometer
0 runs - estimation_procedure: 50 times Clustering
Define a new task