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aloi

aloi

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Author: Anderson Rocha and Siome Goldenstein. Source: Unknown - 2014 Please cite: IEEE Transactions on Neural Networks and Learning Systems, 25(2):289-302, 2014 Multiclass from binary: Expanding one-vs-all, one-vs-one and ECOC-based approaches. Dataset taken from LIBSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass.html In this dataset version, the target attribute is fixed and is given as a nominal feature.

129 features

target (target)nominal1000 unique values
0 missing
0numeric10 unique values
0 missing
1numeric10 unique values
0 missing
2numeric10 unique values
0 missing
3numeric10 unique values
0 missing
4numeric10 unique values
0 missing
5numeric10 unique values
0 missing
6numeric10 unique values
0 missing
7numeric10 unique values
0 missing
8numeric10 unique values
0 missing
9numeric10 unique values
0 missing
10numeric10 unique values
0 missing
11numeric10 unique values
0 missing
12numeric8 unique values
0 missing
13numeric10 unique values
0 missing
14numeric10 unique values
0 missing
15numeric10 unique values
0 missing
16numeric10 unique values
0 missing
17numeric10 unique values
0 missing
18numeric10 unique values
0 missing
19numeric10 unique values
0 missing
20numeric10 unique values
0 missing
21numeric10 unique values
0 missing
22numeric10 unique values
0 missing
23numeric10 unique values
0 missing
24numeric10 unique values
0 missing
25numeric10 unique values
0 missing
26numeric10 unique values
0 missing
27numeric10 unique values
0 missing
28numeric9 unique values
0 missing
29numeric9 unique values
0 missing
30numeric10 unique values
0 missing
31numeric10 unique values
0 missing
32numeric10 unique values
0 missing
33numeric10 unique values
0 missing
34numeric8 unique values
0 missing
35numeric8 unique values
0 missing
36numeric10 unique values
0 missing
37numeric10 unique values
0 missing
38numeric9 unique values
0 missing
39numeric10 unique values
0 missing
40numeric10 unique values
0 missing
41numeric10 unique values
0 missing
42numeric10 unique values
0 missing
43numeric10 unique values
0 missing
44numeric9 unique values
0 missing
45numeric10 unique values
0 missing
46numeric10 unique values
0 missing
47numeric10 unique values
0 missing
48numeric8 unique values
0 missing
49numeric4 unique values
0 missing
50numeric5 unique values
0 missing
51numeric3 unique values
0 missing
52numeric10 unique values
0 missing
53numeric8 unique values
0 missing
54numeric8 unique values
0 missing
55numeric9 unique values
0 missing
56numeric10 unique values
0 missing
57numeric10 unique values
0 missing
58numeric10 unique values
0 missing
59numeric10 unique values
0 missing
60numeric9 unique values
0 missing
61numeric10 unique values
0 missing
62numeric10 unique values
0 missing
63numeric10 unique values
0 missing
64numeric10 unique values
0 missing
65numeric10 unique values
0 missing
66numeric9 unique values
0 missing
67numeric9 unique values
0 missing
68numeric10 unique values
0 missing
69numeric10 unique values
0 missing
70numeric10 unique values
0 missing
71numeric9 unique values
0 missing
72numeric10 unique values
0 missing
73numeric9 unique values
0 missing
74numeric9 unique values
0 missing
75numeric10 unique values
0 missing
76numeric9 unique values
0 missing
77numeric9 unique values
0 missing
78numeric9 unique values
0 missing
79numeric10 unique values
0 missing
80numeric10 unique values
0 missing
81numeric9 unique values
0 missing
82numeric9 unique values
0 missing
83numeric9 unique values
0 missing
84numeric9 unique values
0 missing
85numeric10 unique values
0 missing
86numeric9 unique values
0 missing
87numeric9 unique values
0 missing
88numeric9 unique values
0 missing
89numeric9 unique values
0 missing
90numeric10 unique values
0 missing
91numeric10 unique values
0 missing
92numeric9 unique values
0 missing
93numeric9 unique values
0 missing
94numeric9 unique values
0 missing
95numeric10 unique values
0 missing
96numeric9 unique values
0 missing
97numeric8 unique values
0 missing
98numeric7 unique values
0 missing
99numeric8 unique values
0 missing
100numeric9 unique values
0 missing
101numeric9 unique values
0 missing
102numeric8 unique values
0 missing
103numeric8 unique values
0 missing
104numeric9 unique values
0 missing
105numeric9 unique values
0 missing
106numeric10 unique values
0 missing
107numeric9 unique values
0 missing
108numeric7 unique values
0 missing
109numeric9 unique values
0 missing
110numeric9 unique values
0 missing
111numeric10 unique values
0 missing
112numeric7 unique values
0 missing
113numeric7 unique values
0 missing
114numeric8 unique values
0 missing
115numeric4 unique values
0 missing
116numeric9 unique values
0 missing
117numeric9 unique values
0 missing
118numeric8 unique values
0 missing
119numeric7 unique values
0 missing
120numeric8 unique values
0 missing
121numeric9 unique values
0 missing
122numeric9 unique values
0 missing
123numeric8 unique values
0 missing
124numeric8 unique values
0 missing
125numeric8 unique values
0 missing
126numeric9 unique values
0 missing
127numeric9 unique values
0 missing

19 properties

108000
Number of instances (rows) of the dataset.
129
Number of attributes (columns) of the dataset.
1000
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.
128
Number of numeric attributes.
1
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0.99
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
99.22
Percentage of numeric attributes.
0.1
Percentage of instances belonging to the most frequent class.
0.78
Percentage of nominal attributes.
108
Number of instances belonging to the most frequent class.
0.1
Percentage of instances belonging to the least frequent class.
108
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

10 tasks

0 runs - estimation_procedure: 20% Holdout (Ordered) - target_feature: target
0 runs - estimation_procedure: 10-fold Crossvalidation - evaluation_measure: predictive_accuracy - target_feature: target
0 runs - estimation_procedure: 4-fold Crossvalidation - target_feature: target
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
Define a new task