Data
SensIT-Vehicle-Combined

SensIT-Vehicle-Combined

active Sparse_ARFF Publicly available Visibility: public Uploaded 15-06-2015 by Farooq Zuberi
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Author: University of Wisconsin–Madison libSVM","AAD group Source: [original](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html) - Date unknown Please cite: #Dataset from the LIBSVM data repository. Preprocessing: Regenerate features by the authors' matlab scripts (see Sec. C of Appendix A), then randomly select 10% instances from the noise class so that the class proportion is 1:1:2 (AAV:DW:noise). The training/testing sets are from a random 80% and 20% split of the data.

101 features

class (target)numeric3 unique values
0 missing
att_1numeric77890 unique values
0 missing
att_2numeric74486 unique values
0 missing
att_3numeric80452 unique values
0 missing
att_4numeric79556 unique values
0 missing
att_5numeric70762 unique values
0 missing
att_6numeric74634 unique values
0 missing
att_7numeric72614 unique values
0 missing
att_8numeric75292 unique values
0 missing
att_9numeric74416 unique values
0 missing
att_10numeric72514 unique values
0 missing
att_11numeric70976 unique values
0 missing
att_12numeric70713 unique values
0 missing
att_13numeric69548 unique values
0 missing
att_14numeric66606 unique values
0 missing
att_15numeric64292 unique values
0 missing
att_16numeric63320 unique values
0 missing
att_17numeric62735 unique values
0 missing
att_18numeric62634 unique values
0 missing
att_19numeric62803 unique values
0 missing
att_20numeric63688 unique values
0 missing
att_21numeric62927 unique values
0 missing
att_22numeric62686 unique values
0 missing
att_23numeric62363 unique values
0 missing
att_24numeric61721 unique values
0 missing
att_25numeric61638 unique values
0 missing
att_26numeric61791 unique values
0 missing
att_27numeric61971 unique values
0 missing
att_28numeric61868 unique values
0 missing
att_29numeric61767 unique values
0 missing
att_30numeric62149 unique values
0 missing
att_31numeric62430 unique values
0 missing
att_32numeric62278 unique values
0 missing
att_33numeric62442 unique values
0 missing
att_34numeric62891 unique values
0 missing
att_35numeric62821 unique values
0 missing
att_36numeric63405 unique values
0 missing
att_37numeric64104 unique values
0 missing
att_38numeric63865 unique values
0 missing
att_39numeric63945 unique values
0 missing
att_40numeric64078 unique values
0 missing
att_41numeric64159 unique values
0 missing
att_42numeric64086 unique values
0 missing
att_43numeric63718 unique values
0 missing
att_44numeric63608 unique values
0 missing
att_45numeric63897 unique values
0 missing
att_46numeric63710 unique values
0 missing
att_47numeric63747 unique values
0 missing
att_48numeric63807 unique values
0 missing
att_49numeric63758 unique values
0 missing
att_50numeric63697 unique values
0 missing
att_51numeric71845 unique values
0 missing
att_52numeric69335 unique values
0 missing
att_53numeric70251 unique values
0 missing
att_54numeric80734 unique values
0 missing
att_55numeric82850 unique values
0 missing
att_56numeric80983 unique values
0 missing
att_57numeric77741 unique values
0 missing
att_58numeric75801 unique values
0 missing
att_59numeric72894 unique values
0 missing
att_60numeric70721 unique values
0 missing
att_61numeric69821 unique values
0 missing
att_62numeric69340 unique values
0 missing
att_63numeric69521 unique values
0 missing
att_64numeric68957 unique values
0 missing
att_65numeric68702 unique values
0 missing
att_66numeric68670 unique values
0 missing
att_67numeric68906 unique values
0 missing
att_68numeric68761 unique values
0 missing
att_69numeric69224 unique values
0 missing
att_70numeric69266 unique values
0 missing
att_71numeric69711 unique values
0 missing
att_72numeric69843 unique values
0 missing
att_73numeric69763 unique values
0 missing
att_74numeric69643 unique values
0 missing
att_75numeric69393 unique values
0 missing
att_76numeric69314 unique values
0 missing
att_77numeric69233 unique values
0 missing
att_78numeric69056 unique values
0 missing
att_79numeric68697 unique values
0 missing
att_80numeric68771 unique values
0 missing
att_81numeric68942 unique values
0 missing
att_82numeric68628 unique values
0 missing
att_83numeric68854 unique values
0 missing
att_84numeric68693 unique values
0 missing
att_85numeric68600 unique values
0 missing
att_86numeric68848 unique values
0 missing
att_87numeric68634 unique values
0 missing
att_88numeric68910 unique values
0 missing
att_89numeric68742 unique values
0 missing
att_90numeric68753 unique values
0 missing
att_91numeric68863 unique values
0 missing
att_92numeric68772 unique values
0 missing
att_93numeric68647 unique values
0 missing
att_94numeric68930 unique values
0 missing
att_95numeric68780 unique values
0 missing
att_96numeric68815 unique values
0 missing
att_97numeric68776 unique values
0 missing
att_98numeric68844 unique values
0 missing
att_99numeric68721 unique values
0 missing
att_100numeric68869 unique values
0 missing

62 properties

98528
Number of instances (rows) of the dataset.
101
Number of attributes (columns) of the dataset.
0
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.
101
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0.14
Second quartile (Median) of standard deviation of attributes of the numeric type.
Maximum entropy among attributes.
-1.44
Minimum kurtosis among attributes of the numeric type.
0
Percentage of instances having missing values.
Third quartile of entropy among attributes.
119.15
Maximum kurtosis among attributes of the numeric type.
-0.12
Minimum of means among attributes of the numeric type.
0
Percentage of missing values.
50.48
Third quartile of kurtosis among attributes of the numeric type.
2.27
Maximum of means among attributes of the numeric type.
Minimal mutual information between the nominal attributes and the target attribute.
100
Percentage of numeric attributes.
-0.01
Third quartile of means among attributes of the numeric type.
Maximum mutual information between the nominal attributes and the target attribute.
The minimal number of distinct values among attributes of the nominal type.
0
Percentage of nominal attributes.
Third quartile of mutual information between the nominal attributes and the target attribute.
The maximum number of distinct values among attributes of the nominal type.
-1.43
Minimum skewness among attributes of the numeric type.
First quartile of entropy among attributes.
5
Third quartile of skewness among attributes of the numeric type.
7.98
Maximum skewness among attributes of the numeric type.
0.05
Minimum standard deviation of attributes of the numeric type.
14.85
First quartile of kurtosis among attributes of the numeric type.
0.17
Third quartile of standard deviation of attributes of the numeric type.
0.81
Maximum standard deviation of attributes of the numeric type.
Percentage of instances belonging to the least frequent class.
-0.05
First quartile of means among attributes of the numeric type.
Standard deviation of the number of distinct values among attributes of the nominal type.
Average entropy of the attributes.
Number of instances belonging to the least frequent class.
First quartile of mutual information between the nominal attributes and the target attribute.
33.67
Mean kurtosis among attributes of the numeric type.
0
Number of binary attributes.
2.28
First quartile of skewness among attributes of the numeric type.
0.02
Mean of means among attributes of the numeric type.
0.06
First quartile of standard deviation of attributes of the numeric type.
0.14
Average class difference between consecutive instances.
Average mutual information between the nominal attributes and the target attribute.
Second quartile (Median) of entropy among attributes.
Entropy of the target attribute values.
An estimate of the amount of irrelevant information in the attributes regarding the class. Equals (MeanAttributeEntropy - MeanMutualInformation) divided by MeanMutualInformation.
19.02
Second quartile (Median) of kurtosis among attributes of the numeric type.
0
Number of attributes divided by the number of instances.
Average number of distinct values among the attributes of the nominal type.
-0.03
Second quartile (Median) of means among attributes of the numeric type.
Number of attributes needed to optimally describe the class (under the assumption of independence among attributes). Equals ClassEntropy divided by MeanMutualInformation.
3.33
Mean skewness among attributes of the numeric type.
Second quartile (Median) of mutual information between the nominal attributes and the target attribute.
Percentage of instances belonging to the most frequent class.
0.15
Mean standard deviation of attributes of the numeric type.
3.3
Second quartile (Median) of skewness among attributes of the numeric type.
Number of instances belonging to the most frequent class.
Minimal entropy among attributes.

5 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 10 times 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: class
0 runs - estimation_procedure: 50 times Clustering
0 runs - estimation_procedure: 50 times Clustering
Define a new task