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LED-display-domain-7digit

LED-display-domain-7digit

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1. Title of Database: LED display domain 2. Sources: (a) Breiman,L., Friedman,J.H., Olshen,R.A., & Stone,C.J. (1984). Classification and Regression Trees. Wadsworth International Group: Belmont, California. (see pages 43-49). (b) Donor: David Aha (c) Date: 11/10/1988 3. Past Usage: (many) 1. CART book (above): -- Optimal Bayes classification rate: 74% -- CART decision tree algorithm: 71% (resubstitution estimate) -- Nearest Neighbor Algorithm: 71% -- 200 training and 5000 test instances 2. Quinlan,J.R. (1987). Simplifying Decision Trees. In International Journal of Man-Machine Studies (to appear). -- C4 decision tree algorithm: 72.6% (using pessimistic pruning) -- 2000 training and 500 test instances 3. Tan,M. & Eshelman,L. (1988). Using Weighted Networks to Represent Classification Knowledge in Noisy Domains. In Proceedings of the 5th International Conference on Machine Learning, 121-134, Ann Arbor, Michigan: Morgan Kaufmann. -- IWN system: 73.3% (using the And-OR classification algorithm) -- 400 training and 500 test cases 4. Relevant Information Paragraph: This simple domain contains 7 Boolean attributes and 10 concepts, the set of decimal digits. Recall that LED displays contain 7 light-emitting diodes -- hence the reason for 7 attributes. The problem would be easy if not for the introduction of noise. In this case, each attribute value has the 10% probability of having its value inverted. It's valuable to know the optimal Bayes rate for these databases. In this case, the misclassification rate is 26% (74% classification accuracy). 5. Number of Instances: 500. But in the original URL you can find a C script and run it choosing the number of instances to be generated. 6. Number of Attributes: 7 (all Boolean-valued) 7. Attribute Information: -- All attribute values are either 0 or 1, according to whether the corresponding light is on or not for the decimal digit. -- Each attribute (excluding the class attribute, which is an integer ranging between 0 and 9 inclusive) has a 10% percent chance of being inverted. 8. Missing Attribute Values: None 9. Class Distribution: 10% (Theoretical) -- Each concept (digit) has the same theoretical probability distribution. The program randomly selects the attribute.

8 features

Class (target)nominal10 unique values
0 missing
V1numeric2 unique values
0 missing
V2numeric2 unique values
0 missing
V3numeric2 unique values
0 missing
V4numeric2 unique values
0 missing
V5numeric2 unique values
0 missing
V6numeric2 unique values
0 missing
V7numeric2 unique values
0 missing

107 properties

500
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
10
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.
7
Number of numeric attributes.
1
Number of nominal attributes.
0.72
Average class difference between consecutive instances.
0.89
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
0.31
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
0.65
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
0.89
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
0.31
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
0.65
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
0.89
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
0.31
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
0.65
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
3.31
Entropy of the target attribute values.
0.71
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.8
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.1
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
0.02
Number of attributes divided by the number of instances.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.31
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.65
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
11.4
Percentage of instances belonging to the most frequent class.
57
Number of instances belonging to the most frequent class.
Maximum entropy among attributes.
0.74
Maximum kurtosis among attributes of the numeric type.
0.82
Maximum of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
10
The maximum number of distinct values among attributes of the nominal type.
0.43
Maximum skewness among attributes of the numeric type.
0.49
Maximum standard deviation of attributes of the numeric type.
Average entropy of the attributes.
-1.12
Mean kurtosis among attributes of the numeric type.
0.66
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
10
Average number of distinct values among the attributes of the nominal type.
-0.73
Mean skewness among attributes of the numeric type.
0.46
Mean standard deviation of attributes of the numeric type.
Minimal entropy among attributes.
-1.87
Minimum kurtosis among attributes of the numeric type.
0.4
Minimum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
10
The minimal number of distinct values among attributes of the nominal type.
-1.65
Minimum skewness among attributes of the numeric type.
0.39
Minimum standard deviation of attributes of the numeric type.
7.4
Percentage of instances belonging to the least frequent class.
37
Number of instances belonging to the least frequent class.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.3
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
87.5
Percentage of numeric attributes.
12.5
Percentage of nominal attributes.
First quartile of entropy among attributes.
-1.83
First quartile of kurtosis among attributes of the numeric type.
0.59
First quartile of means among attributes of the numeric type.
First quartile of mutual information between the nominal attributes and the target attribute.
-1.06
First quartile of skewness among attributes of the numeric type.
0.44
First quartile of standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
-1.48
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.67
Second quartile (Median) of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
-0.73
Second quartile (Median) of skewness among attributes of the numeric type.
0.47
Second quartile (Median) of standard deviation of attributes of the numeric type.
Third quartile of entropy among attributes.
-0.87
Third quartile of kurtosis among attributes of the numeric type.
0.73
Third quartile of means among attributes of the numeric type.
Third quartile of mutual information between the nominal attributes and the target attribute.
-0.37
Third quartile of skewness among attributes of the numeric type.
0.49
Third quartile of standard deviation of attributes of the numeric type.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.31
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.31
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.31
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.33
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.64
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0
Standard deviation of the number of distinct values among attributes of the nominal type.
0.92
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.3
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.67
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

11 tasks

0 runs - estimation_procedure: Test on Training Data - target_feature: Class
0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: Class
0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 5 times 2-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: Leave one out - target_feature: Class
0 runs - estimation_procedure: 10% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: 10 times 10-fold Learning Curve - target_feature: Class
0 runs - estimation_procedure: Interleaved Test then Train - target_feature: Class
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