gisette
OpenML dataset with id 41026
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Full work available at URL: https://api.openml.org/data/v1/download/18631146/gisette.sparse_arff
Upload date: 11 February 2018
Dataset Characteristics
Number of classes: 2
Number of features: 5,001 (numeric: 5,000, symbolic: 1 and in total binary: 1 )
Number of instances: 7,000
Number of instances with missing values: 0
Number of missing values: 0
Author: Isabelle Guyon, Steve Gunn, Asa Ben Hur, Gideon Dror Source: LIBSVM Please cite:
GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusable digits '4' and '9'. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.
The digits have been size-normalized and centered in a fixed-size image of dimension 28x28. The original data were modified for the purpose of the feature selection challenge. In particular, pixels were samples at random in the middle top part of the feature containing the information necessary to disambiguate 4 from 9 and higher order features were created as products of these pixels to plunge the problem in a higher dimensional feature space. We also added a number of distractor features called 'probes' having no predictive power. The order of the features and patterns were randomized.
Preprocessing: The data set is also available at UCI. Because the labels of testing set are not available, here we use the validation set (gisette_valid.data and gisette_valid.labels) as the testing set. The training data (gisette_train) are feature-wisely scaled to [-1,1]. Then the testing data (gisette_valid) are scaled based on the same scaling factors for the training data.
Difference with version 1: the target feature is now binary, as it should be.
This page was built for dataset: gisette
