Data
seeds

seeds

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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Author: M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak Source: UCI Please cite: Contributors gratefully acknowledge support of their work by the Institute of Agrophysics of the Polish Academy of Sciences in Lublin. * Title: seeds Data Set * Abstract: Measurements of geometrical properties of kernels belonging to three different varieties of wheat. A soft X-ray technique and GRAINS package were used to construct all seven, real-valued attributes. * Source: Małgorzata Charytanowicz, Jerzy Niewczas Institute of Mathematics and Computer Science, The John Paul II Catholic University of Lublin, Konstantynów 1 H, PL 20-708 Lublin, Poland e-mail: {mchmat,jniewczas}@kul.lublin.pl Piotr Kulczycki, Piotr A. Kowalski, Szymon Lukasik, Slawomir Zak Department of Automatic Control and Information Technology, Cracow University of Technology, Warszawska 24, PL 31-155 Cracow, Poland and Systems Research Institute, Polish Academy of Sciences, Newelska 6, PL 01-447 Warsaw, Poland e-mail: {kulczycki,pakowal,slukasik,slzak}@ibspan.waw.pl * Data Set Information: The examined group comprised kernels belonging to three different varieties of wheat: Kama, Rosa and Canadian, 70 elements each, randomly selected for the experiment. High quality visualization of the internal kernel structure was detected using a soft X-ray technique. It is non-destructive and considerably cheaper than other more sophisticated imaging techniques like scanning microscopy or laser technology. The images were recorded on 13x18 cm X-ray KODAK plates. Studies were conducted using combine harvested wheat grain originating from experimental fields, explored at the Institute of Agrophysics of the Polish Academy of Sciences in Lublin. The data set can be used for the tasks of classification and cluster analysis. * Attribute Information: To construct the data, seven geometric parameters of wheat kernels were measured: 1. area A, 2. perimeter P, 3. compactness C = 4*pi*A/P^2, 4. length of kernel, 5. width of kernel, 6. asymmetry coefficient 7. length of kernel groove. All of these parameters were real-valued continuous. * Relevant Papers: M. Charytanowicz, J. Niewczas, P. Kulczycki, P.A. Kowalski, S. Lukasik, S. Zak, 'A Complete Gradient Clustering Algorithm for Features Analysis of X-ray Images', in: Information Technologies in Biomedicine, Ewa Pietka, Jacek Kawa (eds.), Springer-Verlag, Berlin-Heidelberg, 2010, pp. 15-24.

8 features

Class (target)nominal3 unique values
0 missing
V1numeric193 unique values
0 missing
V2numeric170 unique values
0 missing
V3numeric186 unique values
0 missing
V4numeric188 unique values
0 missing
V5numeric184 unique values
0 missing
V6numeric207 unique values
0 missing
V7numeric148 unique values
0 missing

108 properties

210
Number of instances (rows) of the dataset.
8
Number of attributes (columns) of the dataset.
3
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.
0.93
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk
70
Number of instances belonging to the most frequent class.
NaN
Minimal entropy among attributes.
-0.84
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.81
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
NaN
Maximum entropy among attributes.
-1.11
Minimum kurtosis among attributes of the numeric type.
5.41
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
0.35
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
-0.07
Maximum kurtosis among attributes of the numeric type.
0.87
Minimum of means among attributes of the numeric type.
NaN
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.47
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
14.85
Maximum of means among attributes of the numeric type.
NaN
Minimal mutual information between the nominal attributes and the target attribute.
0.4
Second quartile (Median) of skewness among attributes of the numeric type.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.33
The predictive accuracy obtained by always predicting the majority class.
NaN
Maximum mutual information between the nominal attributes and the target attribute.
3
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of binary attributes.
0.49
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.04
Number of attributes divided by the number of instances.
3
The maximum number of distinct values among attributes of the nominal type.
-0.54
Minimum skewness among attributes of the numeric type.
0
Percentage of instances having missing values.
NaN
Third quartile of entropy among attributes.
0.99
Average class difference between consecutive instances.
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
NaN
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.56
Maximum skewness among attributes of the numeric type.
0.02
Minimum standard deviation of attributes of the numeric type.
0
Percentage of missing values.
-0.14
Third quartile of kurtosis among attributes of the numeric type.
0.94
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
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
2.91
Maximum standard deviation of attributes of the numeric type.
33.33
Percentage of instances belonging to the least frequent class.
87.5
Percentage of numeric attributes.
14.56
Third quartile of means among attributes of the numeric type.
0.09
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
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-1
Average entropy of the attributes.
70
Number of instances belonging to the least frequent class.
12.5
Percentage of nominal attributes.
NaN
Third quartile of mutual information between the nominal attributes and the target attribute.
0.86
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
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
-0.73
Mean kurtosis among attributes of the numeric type.
0.98
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
NaN
First quartile of entropy among attributes.
0.53
Third quartile of skewness among attributes of the numeric type.
0.94
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
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
6.9
Mean of means among attributes of the numeric type.
0.1
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
-1.1
First quartile of kurtosis among attributes of the numeric type.
1.5
Third quartile of standard deviation of attributes of the numeric type.
0.09
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
0.91
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
NaN
Average mutual information between the nominal attributes and the target attribute.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
3.26
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
0.86
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
0.12
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
NaN
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.
NaN
First quartile of mutual information between the nominal attributes and the target attribute.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.94
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
0.82
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.94
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.09
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
3
Average number of distinct values among the attributes of the nominal type.
0.13
First quartile of skewness among attributes of the numeric type.
0.85
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.09
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
Standard deviation of the number of distinct values among attributes of the nominal type.
0.86
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.27
Mean skewness among attributes of the numeric type.
0.38
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
0.86
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
0.96
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
33.33
Percentage of instances belonging to the most frequent class.
1.01
Mean standard deviation of attributes of the numeric type.
NaN
Second quartile (Median) of entropy among attributes.
0.1
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
1.58
Entropy of the target attribute values.
0.05
Error rate achieved by the landmarker weka.classifiers.lazy.IBk

4 tasks

172 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
47 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
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