Dexter

DEXTER is a text classification problem in a bag-of-word representation. This is a two-class classification problem with sparse continuous input variables. This dataset is one of five datasets of the NIPS 2003 feature selection challenge. The original data were formatted by Thorsten Joachims in the “bag-of-words” representation. There were 9947 features (of which 2562 are always zeros for all the examples) representing frequencies of occurrence of word stems in text. The task is to learn which Reuters articles are about 'corporate acquisitions'. We added a number of distractor feature called 'probes' having no predictive power. The order of the features and patterns were randomized. DEXTER -- Positive ex. -- Negative ex. -- Total Training set --150 -- 150 -- 300 Validation set -- 150 -- 150 -- 300 Test set -- 1000 -- 1000 -- 2000 All -- 1300 -- 1300 -- 2600 Number of variables/features/attributes: Real: 9947 Probes: 10053 Total: 20000 This dataset is one of five datasets used in the NIPS 2003 feature selection challenge. Our website http://www.nipsfsc.ecs.soton.ac.uk/ is still open for post-challenge submissions. Information about other related challenges are found at: http://clopinet.com/challenges. The CLOP package includes sample code to process these data: http://clopinet.com/CLOP. All details about the preparation of the data are found in our technical report: Design of experiments for the NIPS 2003 variable selection benchmark, Isabelle Guyon, July 2003, http://www.nipsfsc.ecs.soton.ac.uk/papers/NIPS2003-Datasets.pdf (also included in the dataset archive). Such information was made available only after the end of the challenge. The data are split into training, validation, and test set. Target values are provided only for the 2 first sets. Test set performance results are obtained by submitting prediction results to: http://www.nipsfsc.ecs.soton.ac.uk/. The data are in the following format: dataname.param: Parameters and statistics about the data dataname.feat: Identities of the features (withheld, to avoid biasing feature selection). dataname_train.data: Training set (a sparse matrix, patterns in lines, features in columns: feature number followed by value). dataname_valid.data: Validation set. dataname_test.data: Test set. dataname_train.labels: Labels (truth values of the classes) for training examples. dataname_valid.labels: Validation set labels (withheld during the benchmark, but provided now). dataname_test.labels: Test set labels (withheld, so the data can still be use as a benchmark).