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spectrometer

active ARFF Publicly available Visibility: public Uploaded 25-08-2014 by Hailey
0 likes downloaded by 44 people , 71 total downloads 0 issues 0 downvotes

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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)	nominal	48 unique values 0 missing
LRS-name (ignore)	nominal	531 unique values 0 missing
ID-type	nominal	4 unique values 0 missing
Right-Ascension	numeric	518 unique values 0 missing
Declination	numeric	530 unique values 0 missing
Scale_Factor	numeric	397 unique values 0 missing
Blue_base_1	numeric	461 unique values 0 missing
Blue_base_2	numeric	468 unique values 0 missing
Red_base_1	numeric	426 unique values 0 missing
Red_base_2	numeric	417 unique values 0 missing
blue-band-flux_1	numeric	531 unique values 0 missing
blue-band-flux_2	numeric	531 unique values 0 missing
blue-band-flux_3	numeric	531 unique values 0 missing
blue-band-flux_4	numeric	531 unique values 0 missing
blue-band-flux_5	numeric	531 unique values 0 missing
blue-band-flux_6	numeric	531 unique values 0 missing
blue-band-flux_7	numeric	531 unique values 0 missing
blue-band-flux_8	numeric	531 unique values 0 missing
blue-band-flux_9	numeric	531 unique values 0 missing
blue-band-flux_10	numeric	531 unique values 0 missing
blue-band-flux_11	numeric	531 unique values 0 missing
blue-band-flux_12	numeric	531 unique values 0 missing
blue-band-flux_13	numeric	531 unique values 0 missing
blue-band-flux_14	numeric	531 unique values 0 missing
blue-band-flux_15	numeric	531 unique values 0 missing
blue-band-flux_16	numeric	530 unique values 0 missing
blue-band-flux_17	numeric	531 unique values 0 missing
blue-band-flux_18	numeric	531 unique values 0 missing
blue-band-flux_19	numeric	531 unique values 0 missing
blue-band-flux_20	numeric	531 unique values 0 missing
blue-band-flux_21	numeric	531 unique values 0 missing
blue-band-flux_22	numeric	531 unique values 0 missing
blue-band-flux_23	numeric	531 unique values 0 missing
blue-band-flux_24	numeric	531 unique values 0 missing
blue-band-flux_25	numeric	531 unique values 0 missing
blue-band-flux_26	numeric	531 unique values 0 missing
blue-band-flux_27	numeric	531 unique values 0 missing
blue-band-flux_28	numeric	531 unique values 0 missing
blue-band-flux_29	numeric	531 unique values 0 missing
blue-band-flux_30	numeric	531 unique values 0 missing
blue-band-flux_31	numeric	531 unique values 0 missing
blue-band-flux_32	numeric	531 unique values 0 missing
blue-band-flux_33	numeric	531 unique values 0 missing
blue-band-flux_34	numeric	531 unique values 0 missing
blue-band-flux_35	numeric	531 unique values 0 missing
blue-band-flux_36	numeric	531 unique values 0 missing
blue-band-flux_37	numeric	531 unique values 0 missing
blue-band-flux_38	numeric	531 unique values 0 missing
blue-band-flux_39	numeric	531 unique values 0 missing
blue-band-flux_40	numeric	531 unique values 0 missing
blue-band-flux_41	numeric	531 unique values 0 missing
blue-band-flux_42	numeric	531 unique values 0 missing
blue-band-flux_43	numeric	531 unique values 0 missing
blue-band-flux_44	numeric	531 unique values 0 missing
red-band-flux_1	numeric	531 unique values 0 missing
red-band-flux_2	numeric	531 unique values 0 missing
red-band-flux_3	numeric	531 unique values 0 missing
red-band-flux_4	numeric	531 unique values 0 missing
red-band-flux_5	numeric	531 unique values 0 missing
red-band-flux_6	numeric	531 unique values 0 missing
red-band-flux_7	numeric	531 unique values 0 missing
red-band-flux_8	numeric	531 unique values 0 missing
red-band-flux_9	numeric	531 unique values 0 missing
red-band-flux_10	numeric	531 unique values 0 missing
red-band-flux_11	numeric	531 unique values 0 missing
red-band-flux_12	numeric	531 unique values 0 missing
red-band-flux_13	numeric	531 unique values 0 missing
red-band-flux_14	numeric	531 unique values 0 missing
red-band-flux_15	numeric	531 unique values 0 missing
red-band-flux_16	numeric	531 unique values 0 missing
red-band-flux_17	numeric	531 unique values 0 missing
red-band-flux_18	numeric	531 unique values 0 missing
red-band-flux_19	numeric	531 unique values 0 missing
red-band-flux_20	numeric	531 unique values 0 missing
red-band-flux_21	numeric	531 unique values 0 missing
red-band-flux_22	numeric	531 unique values 0 missing
red-band-flux_23	numeric	531 unique values 0 missing
red-band-flux_24	numeric	531 unique values 0 missing
red-band-flux_25	numeric	531 unique values 0 missing
red-band-flux_26	numeric	531 unique values 0 missing
red-band-flux_27	numeric	531 unique values 0 missing
red-band-flux_28	numeric	531 unique values 0 missing
red-band-flux_29	numeric	531 unique values 0 missing
red-band-flux_30	numeric	531 unique values 0 missing
red-band-flux_31	numeric	531 unique values 0 missing
red-band-flux_32	numeric	531 unique values 0 missing
red-band-flux_33	numeric	531 unique values 0 missing
red-band-flux_34	numeric	531 unique values 0 missing
red-band-flux_35	numeric	531 unique values 0 missing
red-band-flux_36	numeric	531 unique values 0 missing
red-band-flux_37	numeric	531 unique values 0 missing
red-band-flux_38	numeric	531 unique values 0 missing
red-band-flux_39	numeric	531 unique values 0 missing
red-band-flux_40	numeric	531 unique values 0 missing
red-band-flux_41	numeric	531 unique values 0 missing
red-band-flux_42	numeric	531 unique values 0 missing
red-band-flux_43	numeric	531 unique values 0 missing
red-band-flux_44	numeric	531 unique values 0 missing
red-band-flux_45	numeric	531 unique values 0 missing
red-band-flux_46	numeric	531 unique values 0 missing
red-band-flux_47	numeric	531 unique values 0 missing
red-band-flux_48	numeric	531 unique values 0 missing
red-band-flux_49	numeric	531 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

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

100

Number of numeric attributes.

NumberOfSymbolicFeatures

Number of nominal attributes.

AutoCorrelation

0.06

Average class difference between consecutive instances.

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

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

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

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

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

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

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

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

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

ClassEntropy

4.69

Entropy of the target attribute values.

DecisionStumpAUC

0.71

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

DecisionStumpErrRate

0.81

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

DecisionStumpKappa

0.1

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

Dimensionality

0.19

Number of attributes divided by the number of instances.

EquivalentNumberOfAtts

1.83

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

J48.00001.AUC

0.71

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

J48.00001.ErrRate

0.61

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

J48.00001.Kappa

0.36

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

J48.0001.AUC

0.71

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

J48.0001.ErrRate

0.61

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

J48.0001.Kappa

0.36

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

J48.001.AUC

0.71

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

J48.001.ErrRate

0.61

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

J48.001.Kappa

0.36

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

MajorityClassPercentage

10.36

Percentage of instances belonging to the most frequent class.

MajorityClassSize

Number of instances belonging to the most frequent class.

MaxAttributeEntropy

9.05

Maximum entropy among attributes.

MaxKurtosisOfNumericAtts

298.23

Maximum kurtosis among attributes of the numeric type.

MaxMeansOfNumericAtts

12541.21

Maximum of means among attributes of the numeric type.

MaxMutualInformation

4.69

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

MaxNominalAttDistinctValues

531

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

MaxSkewnessOfNumericAtts

15.42

Maximum skewness among attributes of the numeric type.

MaxStdDevOfNumericAtts

5503.17

Maximum standard deviation of attributes of the numeric type.

MeanAttributeEntropy

5.44

Average entropy of the attributes.

MeanKurtosisOfNumericAtts

8.73

Mean kurtosis among attributes of the numeric type.

MeanMeansOfNumericAtts

4677.61

Mean of means among attributes of the numeric type.

MeanMutualInformation

2.56

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

MeanNoiseToSignalRatio

1.13

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

MeanNominalAttDistinctValues

194.33

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

MeanSkewnessOfNumericAtts

1.12

Mean skewness among attributes of the numeric type.

MeanStdDevOfNumericAtts

1314.82

Mean standard deviation of attributes of the numeric type.

MinAttributeEntropy

1.83

Minimal entropy among attributes.

MinKurtosisOfNumericAtts

-1.18

Minimum kurtosis among attributes of the numeric type.

MinMeansOfNumericAtts

-9.85

Minimum of means among attributes of the numeric type.

MinMutualInformation

0.43

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

MinNominalAttDistinctValues

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

MinSkewnessOfNumericAtts

-1.77

Minimum skewness among attributes of the numeric type.

MinStdDevOfNumericAtts

Minimum standard deviation of attributes of the numeric type.

MinorityClassPercentage

0.19

Percentage of instances belonging to the least frequent class.

MinorityClassSize

Number of instances belonging to the least frequent class.

NaiveBayesAUC

0.82

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

NaiveBayesErrRate

0.73

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

NaiveBayesKappa

0.24

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

NumberOfBinaryFeatures

Number of binary attributes.

PercentageOfBinaryFeatures

Percentage of binary attributes.

PercentageOfInstancesWithMissingValues

Percentage of instances having missing values.

PercentageOfMissingValues

Percentage of missing values.

PercentageOfNumericFeatures

98.04

Percentage of numeric attributes.

PercentageOfSymbolicFeatures

1.96

Percentage of nominal attributes.

Quartile1AttributeEntropy

1.83

First quartile of entropy among attributes.

Quartile1KurtosisOfNumericAtts

2.46

First quartile of kurtosis among attributes of the numeric type.

Quartile1MeansOfNumericAtts

1834.51

First quartile of means among attributes of the numeric type.

Quartile1MutualInformation

0.43

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

Quartile1SkewnessOfNumericAtts

-1.05

First quartile of skewness among attributes of the numeric type.

Quartile1StdDevOfNumericAtts

820.13

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

Quartile2AttributeEntropy

5.44

Second quartile (Median) of entropy among attributes.

Quartile2KurtosisOfNumericAtts

4.24

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

Quartile2MeansOfNumericAtts

3765.59

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

Quartile2MutualInformation

2.56

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

Quartile2SkewnessOfNumericAtts

1.44

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

Quartile2StdDevOfNumericAtts

1033.12

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

Quartile3AttributeEntropy

9.05

Third quartile of entropy among attributes.

Quartile3KurtosisOfNumericAtts

8.51

Third quartile of kurtosis among attributes of the numeric type.

Quartile3MeansOfNumericAtts

7359.54

Third quartile of means among attributes of the numeric type.

Quartile3MutualInformation

4.69

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

Quartile3SkewnessOfNumericAtts

2.25

Third quartile of skewness among attributes of the numeric type.

Quartile3StdDevOfNumericAtts

1396.74

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

REPTreeDepth1AUC

0.48

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

REPTreeDepth1ErrRate

0.9

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

REPTreeDepth1Kappa

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

REPTreeDepth2AUC

0.48

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

REPTreeDepth2ErrRate

0.9

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

REPTreeDepth2Kappa

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

REPTreeDepth3AUC

0.48

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

REPTreeDepth3ErrRate

0.9

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

REPTreeDepth3Kappa

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

RandomTreeDepth1AUC

0.7

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

RandomTreeDepth1ErrRate

0.72

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

RandomTreeDepth1Kappa

0.23

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

RandomTreeDepth2AUC

0.7

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

RandomTreeDepth2ErrRate

0.72

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

RandomTreeDepth2Kappa

0.23

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

RandomTreeDepth3AUC

0.7

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

RandomTreeDepth3ErrRate

0.72

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

RandomTreeDepth3Kappa

0.23

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

StdvNominalAttDistinctValues

292.39

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

kNN1NAUC

0.67

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

kNN1NErrRate

0.63

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

kNN1NKappa

0.33

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

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

Clustering on spectrometer