46
gina_agnostic
1
**Author**:
**Source**: Unknown - Date unknown
**Please cite**:
Datasets from the Agnostic Learning vs. Prior Knowledge Challenge (http://www.agnostic.inf.ethz.ch)
Dataset from: http://www.agnostic.inf.ethz.ch/datasets.php
Modified by TunedIT (converted to ARFF format)
GINA is digit recognition database
The task of GINA is handwritten digit recognition. For the "agnostic learning track" we chose the problem of separating two-digit odd numbers from two-digit even numbers. Only the unit digit is informative for that task, therefore at least 1/2 of the features are distracters. Additionally, the pixels that are almost always blank were removed and the pixel order was randomized to hide the feature identity. This is a two class classification problem with sparse continuous input variables, in which each class is composed of several clusters. It is a problem with heterogeneous classes.
Data type: non-sparse
Number of features: 970
Number of examples and check-sums:
Pos_ex Neg_ex Tot_ex Check_sum
Train 1550 1603 3153 164947945.00
Valid 155 160 315 16688946.00
This dataset contains samples from both training and validation datasets.
1
ARFF
2014-10-06T23:56:01
Public https://test.openml.org/data/v1/download/46/gina_agnostic.arff
46 label study_14 public active
2024-01-10 13:49:30 e8b42f51e0a453e4b77c24f8e2569948