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glass

glass

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Author: Source: Unknown - Please cite: 1. Title: Glass Identification Database 2. Sources: (a) Creator: B. German -- Central Research Establishment Home Office Forensic Science Service Aldermaston, Reading, Berkshire RG7 4PN (b) Donor: Vina Spiehler, Ph.D., DABFT Diagnostic Products Corporation (213) 776-0180 (ext 3014) (c) Date: September, 1987 3. Past Usage: -- Rule Induction in Forensic Science -- Ian W. Evett and Ernest J. Spiehler -- Central Research Establishment Home Office Forensic Science Service Aldermaston, Reading, Berkshire RG7 4PN -- Unknown technical note number (sorry, not listed here) -- General Results: nearest neighbor held its own with respect to the rule-based system 4. Relevant Information:n Vina conducted a comparison test of her rule-based system, BEAGLE, the nearest-neighbor algorithm, and discriminant analysis. BEAGLE is a product available through VRS Consulting, Inc.; 4676 Admiralty Way, Suite 206; Marina Del Ray, CA 90292 (213) 827-7890 and FAX: -3189. In determining whether the glass was a type of "float" glass or not, the following results were obtained (# incorrect answers): Type of Sample Beagle NN DA Windows that were float processed (87) 10 12 21 Windows that were not: (76) 19 16 22 The study of classification of types of glass was motivated by criminological investigation. At the scene of the crime, the glass left can be used as evidence...if it is correctly identified! 5. Number of Instances: 214 6. Number of Attributes: 10 (including an Id#) plus the class attribute -- all attributes are continuously valued 7. Attribute Information: 1. Id number: 1 to 214 2. RI: refractive index 3. Na: Sodium (unit measurement: weight percent in corresponding oxide, as are attributes 4-10) 4. Mg: Magnesium 5. Al: Aluminum 6. Si: Silicon 7. K: Potassium 8. Ca: Calcium 9. Ba: Barium 10. Fe: Iron 11. Type of glass: (class attribute) -- 1 building_windows_float_processed -- 2 building_windows_non_float_processed -- 3 vehicle_windows_float_processed -- 4 vehicle_windows_non_float_processed (none in this database) -- 5 containers -- 6 tableware -- 7 headlamps 8. Missing Attribute Values: None Summary Statistics: Attribute: Min Max Mean SD Correlation with class 2. RI: 1.5112 1.5339 1.5184 0.0030 -0.1642 3. Na: 10.73 17.38 13.4079 0.8166 0.5030 4. Mg: 0 4.49 2.6845 1.4424 -0.7447 5. Al: 0.29 3.5 1.4449 0.4993 0.5988 6. Si: 69.81 75.41 72.6509 0.7745 0.1515 7. K: 0 6.21 0.4971 0.6522 -0.0100 8. Ca: 5.43 16.19 8.9570 1.4232 0.0007 9. Ba: 0 3.15 0.1750 0.4972 0.5751 10. Fe: 0 0.51 0.0570 0.0974 -0.1879 9. Class Distribution: (out of 214 total instances) -- 163 Window glass (building windows and vehicle windows) -- 87 float processed -- 70 building windows -- 17 vehicle windows -- 76 non-float processed -- 76 building windows -- 0 vehicle windows -- 51 Non-window glass -- 13 containers -- 9 tableware -- 29 headlamps Relabeled values in attribute 'Type' From: '1' To: 'build wind float' From: '2' To: 'build wind non-float' From: '3' To: 'vehic wind float' From: '4' To: 'vehic wind non-float' From: '5' To: containers From: '6' To: tableware From: '7' To: headlamps

10 features

Type (target)nominal6 unique values
0 missing
RInumeric178 unique values
0 missing
Nanumeric142 unique values
0 missing
Mgnumeric94 unique values
0 missing
Alnumeric118 unique values
0 missing
Sinumeric133 unique values
0 missing
Knumeric65 unique values
0 missing
Canumeric143 unique values
0 missing
Banumeric34 unique values
0 missing
Fenumeric32 unique values
0 missing
Show all 10 features

107 properties

NumberOfInstances
214
Number of instances (rows) of the dataset.
NumberOfFeatures
10
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
9
Number of numeric attributes.
NumberOfSymbolicFeatures
1
Number of nominal attributes.
AutoCorrelation
0.26
Average class difference between consecutive instances.
CfsSubsetEval_DecisionStumpAUC
0.78
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
0.36
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
0.5
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
0.78
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
0.36
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
0.5
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
0.78
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
0.36
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
0.5
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
2.18
Entropy of the target attribute values.
DecisionStumpAUC
0.7
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpErrRate
0.57
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
DecisionStumpKappa
0.23
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
Dimensionality
0.05
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
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.00001.Kappa
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
J48.0001.AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.0001.Kappa
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
J48.001.AUC
0.8
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.ErrRate
0.36
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
J48.001.Kappa
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
MajorityClassPercentage
35.51
Percentage of instances belonging to the most frequent class.
MajorityClassSize
76
Number of instances belonging to the most frequent class.
MaxAttributeEntropy
Maximum entropy among attributes.
MaxKurtosisOfNumericAtts
54.69
Maximum kurtosis among attributes of the numeric type.
MaxMeansOfNumericAtts
72.65
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
6.55
Maximum skewness among attributes of the numeric type.
MaxStdDevOfNumericAtts
1.44
Maximum standard deviation of attributes of the numeric type.
MeanAttributeEntropy
Average entropy of the attributes.
MeanKurtosisOfNumericAtts
9.91
Mean kurtosis among attributes of the numeric type.
MeanMeansOfNumericAtts
11.27
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
1.65
Mean skewness among attributes of the numeric type.
MeanStdDevOfNumericAtts
0.69
Mean standard deviation of attributes of the numeric type.
MinAttributeEntropy
Minimal entropy among attributes.
MinKurtosisOfNumericAtts
-0.41
Minimum kurtosis among attributes of the numeric type.
MinMeansOfNumericAtts
0.06
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
-1.15
Minimum skewness among attributes of the numeric type.
MinStdDevOfNumericAtts
0
Minimum standard deviation of attributes of the numeric type.
MinorityClassPercentage
4.21
Percentage of instances belonging to the least frequent class.
MinorityClassSize
9
Number of instances belonging to the least frequent class.
NaiveBayesAUC
0.72
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesErrRate
0.5
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaiveBayesKappa
0.32
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
90
Percentage of numeric attributes.
PercentageOfSymbolicFeatures
10
Percentage of nominal attributes.
Quartile1AttributeEntropy
First quartile of entropy among attributes.
Quartile1KurtosisOfNumericAtts
2.36
First quartile of kurtosis among attributes of the numeric type.
Quartile1MeansOfNumericAtts
0.34
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
-0.14
First quartile of skewness among attributes of the numeric type.
Quartile1StdDevOfNumericAtts
0.3
First quartile of standard deviation of attributes of the numeric type.
Quartile2AttributeEntropy
Second quartile (Median) of entropy among attributes.
Quartile2KurtosisOfNumericAtts
3.05
Second quartile (Median) of kurtosis among attributes of the numeric type.
Quartile2MeansOfNumericAtts
1.52
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
1.63
Second quartile (Median) of skewness among attributes of the numeric type.
Quartile2StdDevOfNumericAtts
0.65
Second quartile (Median) of standard deviation of attributes of the numeric type.
Quartile3AttributeEntropy
Third quartile of entropy among attributes.
Quartile3KurtosisOfNumericAtts
9.61
Third quartile of kurtosis among attributes of the numeric type.
Quartile3MeansOfNumericAtts
11.18
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
2.73
Third quartile of skewness among attributes of the numeric type.
Quartile3StdDevOfNumericAtts
1.12
Third quartile of standard deviation of attributes of the numeric type.
REPTreeDepth1AUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth1Kappa
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
REPTreeDepth2AUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth2Kappa
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
REPTreeDepth3AUC
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3ErrRate
0.4
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
REPTreeDepth3Kappa
0.43
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
RandomTreeDepth1AUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth1Kappa
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
RandomTreeDepth2AUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth2Kappa
0.53
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
RandomTreeDepth3AUC
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3ErrRate
0.35
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
RandomTreeDepth3Kappa
0.53
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
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NErrRate
0.33
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
kNN1NKappa
0.55
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
Show all 107 properties

20 tasks

Supervised Classification on glass
684 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Type
Supervised Classification on glass
325 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Type
Supervised Classification on glass
321 runs - estimation_procedure: 5 times 2-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Type
Supervised Classification on glass
169 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Type
Supervised Classification on glass
0 runs - estimation_procedure: Leave one out - evaluation_measure: predictive_accuracy - target_feature: Type
Supervised Classification on glass
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: Type
Learning Curve on glass
179 runs - estimation_procedure: 10 times 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Type
Learning Curve on glass
78 runs - estimation_procedure: 10-fold Learning Curve - evaluation_measure: predictive_accuracy - target_feature: Type
Supervised Data Stream Classification on glass
24 runs - estimation_procedure: Interleaved Test then Train - target_feature: Type
Clustering on glass
0 runs
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
Clustering on glass
0 runs - estimation_procedure: 50 times Clustering
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