Data
artificial-characters

artificial-characters

active ARFF Publicly available Visibility: public Uploaded 21-05-2015 by Rafael G. Mantovani
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  • artificial OpenML100 study_123 study_135 study_14 study_50 study_52 uci
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Author: H. Altay Guvenir, Burak Acar, Haldun Muderrisoglu Source: [UCI](https://archive.ics.uci.edu/ml/datasets/Artificial+Characters) - 1992 Please cite: [UCI](https://archive.ics.uci.edu/ml/citation_policy.html) This database has been artificially generated. It describes the structure of the capital letters A, C, D, E, F, G, H, L, P, R, indicated by a number 1-10, in that order (A=1,C=2,...). Each letter's structure is described by a set of segments (lines) which resemble the way an automatic program would segment an image. The dataset consists of 600 such descriptions per letter. Originally, each 'instance' (letter) was stored in a separate file, each consisting of between 1 and 7 segments, numbered 0,1,2,3,... Here they are merged. That means that the first 5 instances describe the first 5 segments of the first segmentation of the first letter (A). Also, the training set (100 examples) and test set (the rest) are merged. The next 7 instances describe another segmentation (also of the letter A) and so on. ### Attribute Information * V1: object number, the number of the segment (0,1,2,..,7) * V2-V5: the initial and final coordinates of a segment in a cartesian plane (XX1,YY1,XX2,YY2). * V6: size, this is the length of a segment computed by using the geometric distance between two points A(X1,Y1) and B(X2,Y2). * V7: diagonal, this is the length of the diagonal of the smallest rectangle which includes the picture of the character. The value of this attribute is the same in each object. ### Relevant Papers M. Botta, A. Giordana, L. Saitta: "Learning Fuzzy Concept Definitions", IEEE-Fuzzy Conference, 1993. M. Botta, A. Giordana: "Learning Quantitative Feature in a Symbolic Environment", LNAI 542, 1991, pp. 296-305.

8 features

Class (target)nominal10 unique values
0 missing
V1numeric8 unique values
0 missing
V2numeric45 unique values
0 missing
V3numeric63 unique values
0 missing
V4numeric48 unique values
0 missing
V5numeric66 unique values
0 missing
V6numeric333 unique values
0 missing
V7numeric511 unique values
0 missing

107 properties

10218
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.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
0
Number of binary attributes.
First quartile of mutual information between the nominal attributes and the target attribute.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.53
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
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
10
Average number of distinct values among the attributes of the nominal type.
0.35
First quartile of skewness among attributes of the numeric type.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.91
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.67
Mean skewness among attributes of the numeric type.
7.8
First quartile of standard deviation of attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.42
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
0.87
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
9.5
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of entropy among attributes.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.53
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
0.21
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
13.86
Percentage of instances belonging to the most frequent class.
Minimal entropy among attributes.
-0.23
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
3.28
Entropy of the target attribute values.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
1416
Number of instances belonging to the most frequent class.
-0.55
Minimum kurtosis among attributes of the numeric type.
15.25
Second quartile (Median) of means among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.6
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
Maximum entropy among attributes.
2.22
Minimum of means among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.35
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.83
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
1.25
Maximum kurtosis among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
0.71
Second quartile (Median) of skewness among attributes of the numeric type.
0.6
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.08
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
40.46
Maximum of means among attributes of the numeric type.
10
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
9.73
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0
Number of attributes divided by the number of instances.
Maximum mutual information between the nominal attributes and the target attribute.
0.2
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
10
The maximum number of distinct values among attributes of the nominal type.
1.72
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
0.26
Third quartile of kurtosis among attributes of the numeric type.
1
Average class difference between consecutive instances.
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
1.42
Maximum skewness among attributes of the numeric type.
5.87
Percentage of instances belonging to the least frequent class.
87.5
Percentage of numeric attributes.
21.03
Third quartile of means among attributes of the numeric type.
0.91
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
14.18
Maximum standard deviation of attributes of the numeric type.
600
Number of instances belonging to the least frequent class.
12.5
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
0.42
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
Average entropy of the attributes.
0.77
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
First quartile of entropy among attributes.
0.79
Third quartile of skewness among attributes of the numeric type.
0.53
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
0.76
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
-0.04
Mean kurtosis among attributes of the numeric type.
0.7
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-0.53
First quartile of kurtosis among attributes of the numeric type.
13.1
Third quartile of standard deviation of attributes of the numeric type.
0.91
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
0.88
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.3
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
15.68
Mean of means among attributes of the numeric type.
Average mutual information between the nominal attributes and the target attribute.
0.22
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
6.06
First quartile of means among attributes of the numeric type.
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.42
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
0.21
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.66
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001

48 tasks

7700 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
38 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 10-fold Learning Curve - target_feature:
0 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1299 runs - target_feature: Class
1298 runs - target_feature: Class
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