Donated on 2/28/2008

ARCENE's task is to distinguish cancer versus normal patterns from mass-spectrometric data. This is a two-class classification problem with continuous input variables. This dataset is one of 5 datasets of the NIPS 2003 feature selection challenge.

Dataset Characteristics


Subject Area

Health and Medicine

Associated Tasks


Feature Type


# Instances


# Features


Dataset Information

Additional Information

ARCENE was obtained by merging three mass-spectrometry datasets to obtain enough training and test data for a benchmark. The original features indicate the abundance of proteins in human sera having a given mass value. Based on those features one must separate cancer patients from healthy patients. We added a number of distractor feature called 'probes' having no predictive power. The order of the features and patterns were randomized. ARCENE -- Positive ex. -- Negative ex. -- Total Training set -- 44 -- 56 -- 100 Validation set -- 44 -- 56 -- 100 Test set -- 310 -- 390 -- 700 All -- 398 -- 502 -- 900 Number of variables/features/attributes: Real: 7000 Probes: 3000 Total: 10000 This dataset is one of five datasets used in the NIPS 2003 feature selection challenge. Our website is still open for post-challenge submissions. Information about other related challenges are found at: The CLOP package includes sample code to process these data: 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, (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: 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). Training set (a coma delimited regular matrix, patterns in lines, features in columns). Validation set. 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).

Has Missing Values?


Variable Information

We do not provide attribute information to avoid biasing the feature selection process.


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Isabelle Guyon

Steve Gunn

Asa Ben-Hur

Gideon Dror


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