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qualitative-bankruptcy

qualitative-bankruptcy

active ARFF Publicly available Visibility: public Uploaded 25-05-2015 by Rafael G. Mantovani
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Author: A. Martin, J. Uthayakumar, M. Nadarajan, V. Prasanna Venkatesan Source: UCI Please cite: * Abstract: Predict the Bankruptcy from Qualitative parameters from experts. * Source: Source Information -- Creator : Mr.A.Martin(jayamartin '@' yahoo.com) Mr.J.Uthayakumar (uthayakumar17691 '@' gmail.com) Mr.M.Nadarajan(nadaraj.muthuvel '@' gmail.com) -- Guided By : Dr.V.Prasanna Venkatesan -- Institution : Sri Manakula Vinayagar Engineering College and Pondicherry University -- Country : India -- Date : February 2014 * Data Set Information: The parameters which we used for collecting the dataset is referred from the paper 'The discovery of expert' decision rules from qualitative bankruptcy data using genetic algorithms' by Myoung-Jong Kim*, Ingoo Han. * Attribute Information: (P=Positive,A-Average,N-negative,B-Bankruptcy,NB-Non-Bankruptcy) 1. Industrial Risk: {P,A,N} 2. Management Risk: {P,A,N} 3. Financial Flexibility: {P,A,N} 4. Credibility: {P,A,N} 5. Competitiveness: {P,A,N} 6. Operating Risk: {P,A,N} 7. Class: {B,NB} * Relevant Papers: The parameters which we used for collecting the dataset is referred from the paper 'The discovery of experts’ decision rules from qualitative bankruptcy data using genetic algorithms' by Myoung-Jong Kim*, Ingoo Han.

7 features

Class (target)nominal2 unique values
0 missing
V1nominal3 unique values
0 missing
V2nominal3 unique values
0 missing
V3nominal3 unique values
0 missing
V4nominal3 unique values
0 missing
V5nominal3 unique values
0 missing
V6nominal3 unique values
0 missing

62 properties

250
Number of instances (rows) of the dataset.
7
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.
7
Number of nominal attributes.
14.29
Percentage of binary attributes.
Second quartile (Median) of standard deviation of attributes of the numeric type.
1.58
Maximum entropy among attributes.
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
1.58
Third quartile of entropy among attributes.
Maximum kurtosis among attributes of the numeric type.
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
Third quartile of kurtosis among attributes of the numeric type.
Maximum of means among attributes of the numeric type.
0.05
Minimal mutual information between the nominal attributes and the target attribute.
0
Percentage of numeric attributes.
Third quartile of means among attributes of the numeric type.
0.9
Maximum mutual information between the nominal attributes and the target attribute.
2
The minimal number of distinct values among attributes of the nominal type.
100
Percentage of nominal attributes.
0.66
Third quartile of mutual information between the nominal attributes and the target attribute.
3
The maximum number of distinct values among attributes of the nominal type.
Minimum skewness among attributes of the numeric type.
1.52
First quartile of entropy among attributes.
Third quartile of skewness among attributes of the numeric type.
Maximum skewness among attributes of the numeric type.
Minimum standard deviation of attributes of the numeric type.
First quartile of kurtosis among attributes of the numeric type.
Third quartile of standard deviation of attributes of the numeric type.
Maximum standard deviation of attributes of the numeric type.
42.8
Percentage of instances belonging to the least frequent class.
First quartile of means among attributes of the numeric type.
0.38
Standard deviation of the number of distinct values among attributes of the nominal type.
1.54
Average entropy of the attributes.
107
Number of instances belonging to the least frequent class.
0.06
First quartile of mutual information between the nominal attributes and the target attribute.
Mean kurtosis among attributes of the numeric type.
1
Number of binary attributes.
First quartile of skewness among attributes of the numeric type.
Mean of means among attributes of the numeric type.
First quartile of standard deviation of attributes of the numeric type.
1
Average class difference between consecutive instances.
0.37
Average mutual information between the nominal attributes and the target attribute.
1.53
Second quartile (Median) of entropy among attributes.
0.98
Entropy of the target attribute values.
3.16
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
Second quartile (Median) of kurtosis among attributes of the numeric type.
0.03
Number of attributes divided by the number of instances.
2.86
Average number of distinct values among the attributes of the nominal type.
Second quartile (Median) of means among attributes of the numeric type.
2.65
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
Mean skewness among attributes of the numeric type.
0.32
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
57.2
Percentage of instances belonging to the most frequent class.
Mean standard deviation of attributes of the numeric type.
Second quartile (Median) of skewness among attributes of the numeric type.
143
Number of instances belonging to the most frequent class.
1.52
Minimal entropy among attributes.

6 tasks

116 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: Class
31 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Class
0 runs - estimation_procedure: 33% Holdout set - target_feature: Class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
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