Data
codrnaNorm

codrnaNorm

active Sparse_ARFF Publicly available Visibility: public Uploaded 14-02-2015 by Jan van Rijn
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Normalized form of codrna (351) Author: Andrew V Uzilov","Joshua M Keegan","David H Mathews. Source: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets) - Please cite: [AVU06a] Andrew V Uzilov, Joshua M Keegan, and David H Mathews. Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change. BMC Bioinformatics, 7(173), 2006. This is the cod-rna dataset, retrieved 2014-11-14 from the libSVM site. Additional to the preprocessing done there (see LibSVM site for details), this dataset was created as follows: -join test, train and rest datasets -normalize each file columnwise according to the following rules: -If a column only contains one value (constant feature), it will set to zero and thus removed by sparsity. -If a column contains two values (binary feature), the value occuring more often will be set to zero, the other to one. -If a column contains more than two values (multinary/real feature), the column is divided by its std deviation. NOTE: please keep in mind that cod-rna has many duplicated data points, within each file (train,test,rest) and also accross these files. these duplicated points have not been removed!

9 features

codrna_Y (target)nominal2 unique values
0 missing
codrna_X1numeric1327 unique values
0 missing
codrna_X2numeric43 unique values
0 missing
codrna_X3numeric228 unique values
0 missing
codrna_X4numeric228 unique values
0 missing
codrna_X5numeric220 unique values
0 missing
codrna_X6numeric228 unique values
0 missing
codrna_X7numeric228 unique values
0 missing
codrna_X8numeric220 unique values
0 missing

19 properties

488565
Number of instances (rows) of the dataset.
9
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.
8
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of instances having missing values.
0.56
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
88.89
Percentage of numeric attributes.
66.67
Percentage of instances belonging to the most frequent class.
11.11
Percentage of nominal attributes.
325710
Number of instances belonging to the most frequent class.
33.33
Percentage of instances belonging to the least frequent class.
162855
Number of instances belonging to the least frequent class.
1
Number of binary attributes.
11.11
Percentage of binary attributes.

13 tasks

0 runs - estimation_procedure: 33% Holdout set - evaluation_measure: predictive_accuracy - target_feature: codrna_Y
309 runs - estimation_procedure: Interleaved Test then Train - target_feature: codrna_Y
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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