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dbworld-subjects

dbworld-subjects

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Author: Michele Filannino Source: UCI Please cite: * Dataset: DBworld e-mails data set Task: dbworld-subjects * Source: Michele Filannino, PhD University of Manchester Centre for Doctoral Training Email: filannim_AT_cs.man.ac.uk * Data Set Information: I collected 64 e-mails from DBWorld newsletter and I used them to train different algorithms in order to classify between 'announces of conferences' and 'everything else'. I used a binary bag-of-words representation with a stopword removal pre-processing task before. * Attribute Information: Each attribute corresponds to a precise word or stem in the entire data set vocabulary (I used bag-of-words representation). * Relevant Papers: Michele Filannino, 'DBWorld e-mail classification using a very small corpus', Project of Machine Learning course, University of Manchester, 2011.

243 features

Class (target)nominal2 unique values
0 missing
V1nominal2 unique values
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V2nominal2 unique values
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V3nominal2 unique values
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V4nominal2 unique values
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V5nominal2 unique values
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V21nominal2 unique values
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V22nominal2 unique values
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V23nominal2 unique values
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V24nominal2 unique values
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V25nominal2 unique values
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V26nominal2 unique values
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V27nominal2 unique values
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V28nominal2 unique values
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V32nominal2 unique values
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V33nominal2 unique values
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V34nominal2 unique values
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V35nominal2 unique values
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V36nominal2 unique values
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V37nominal2 unique values
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V38nominal2 unique values
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V107nominal2 unique values
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V110nominal2 unique values
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V111nominal2 unique values
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V112nominal2 unique values
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V113nominal2 unique values
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V114nominal2 unique values
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V115nominal2 unique values
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V116nominal2 unique values
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V120nominal2 unique values
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V123nominal2 unique values
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V128nominal2 unique values
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V186nominal2 unique values
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V192nominal2 unique values
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V211nominal2 unique values
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V212nominal2 unique values
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V214nominal2 unique values
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V215nominal2 unique values
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V216nominal2 unique values
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V217nominal2 unique values
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V218nominal2 unique values
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V219nominal2 unique values
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V220nominal2 unique values
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V223nominal2 unique values
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V224nominal2 unique values
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V225nominal2 unique values
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V226nominal2 unique values
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V227nominal2 unique values
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V228nominal2 unique values
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V229nominal2 unique values
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V230nominal2 unique values
0 missing
V231nominal2 unique values
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V232nominal2 unique values
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V233nominal2 unique values
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V234nominal2 unique values
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V235nominal2 unique values
0 missing
V236nominal2 unique values
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V237nominal2 unique values
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V238nominal2 unique values
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V239nominal2 unique values
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V240nominal2 unique values
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V241nominal2 unique values
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V242nominal2 unique values
0 missing

107 properties

64
Number of instances (rows) of the dataset.
243
Number of attributes (columns) of the dataset.
2
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.
0
Number of numeric attributes.
243
Number of nominal attributes.
0.94
Average class difference between consecutive instances.
0.82
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
0.23
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
0.54
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
0.82
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
0.23
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
0.54
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
0.82
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
0.23
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
0.54
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
0.99
Entropy of the target attribute values.
0.67
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.DecisionStump
0.38
Error rate achieved by the landmarker weka.classifiers.trees.DecisionStump
0.25
Kappa coefficient achieved by the landmarker weka.classifiers.trees.DecisionStump
3.8
Number of attributes divided by the number of instances.
44.94
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .00001
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .0001
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.28
Error rate achieved by the landmarker weka.classifiers.trees.J48 -C .001
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.J48 -C .001
54.69
Percentage of instances belonging to the most frequent class.
35
Number of instances belonging to the most frequent class.
0.81
Maximum entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
0.23
Maximum mutual information between the nominal attributes and the target attribute.
2
The maximum number of distinct values among attributes of the nominal type.
Maximum skewness among attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
0.16
Average entropy of the attributes.
Mean kurtosis among attributes of the numeric type.
Mean of means among attributes of the numeric type.
0.02
Average mutual information between the nominal attributes and the target attribute.
6.38
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
2
Average number of distinct values among the attributes of the nominal type.
Mean skewness among attributes of the numeric type.
Mean standard deviation of attributes of the numeric type.
0.12
Minimal entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Minimal mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
45.31
Percentage of instances belonging to the least frequent class.
29
Number of instances belonging to the least frequent class.
0.93
Area Under the ROC Curve achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.23
Error rate achieved by the landmarker weka.classifiers.bayes.NaiveBayes
0.51
Kappa coefficient achieved by the landmarker weka.classifiers.bayes.NaiveBayes
243
Number of binary attributes.
100
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
0
Percentage of numeric attributes.
100
Percentage of nominal attributes.
0.12
First quartile of entropy among attributes.
First quartile of kurtosis among attributes of the numeric type.
First quartile of means among attributes of the numeric type.
0.01
First quartile of mutual information between the nominal attributes and the target attribute.
First quartile of skewness among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
0.12
Second quartile (Median) of entropy among attributes.
Second quartile (Median) of kurtosis among attributes of the numeric type.
Second quartile (Median) of means among attributes of the numeric type.
0.02
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of skewness among attributes of the numeric type.
Second quartile (Median) of standard deviation of attributes of the numeric type.
0.2
Third quartile of entropy among attributes.
Third quartile of kurtosis among attributes of the numeric type.
Third quartile of means among attributes of the numeric type.
0.02
Third quartile of mutual information between the nominal attributes and the target attribute.
Third quartile of skewness among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 1
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 2
0.79
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.28
Error rate achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.45
Kappa coefficient achieved by the landmarker weka.classifiers.trees.REPTree -L 3
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 1
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.52
Kappa coefficient achieved by the landmarker weka.classifiers.trees.RandomTree -depth 2
0.76
Area Under the ROC Curve achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.23
Error rate achieved by the landmarker weka.classifiers.trees.RandomTree -depth 3
0.52
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.
0.89
Area Under the ROC Curve achieved by the landmarker weka.classifiers.lazy.IBk
0.19
Error rate achieved by the landmarker weka.classifiers.lazy.IBk
0.63
Kappa coefficient achieved by the landmarker weka.classifiers.lazy.IBk

13 tasks

31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
9 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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