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The goal is to provide the best possible predictive models for the five tasks of the challenge using or not prior knowledge about the tasks. All tasks are 2-class classification problems.
Are there prizes?
Yes: A cash award and a certificate will be conferred to the winner(s) at IJCNN 2007. There will be several prizes:
- One award for the best overall entry in the agnostic learning track.
- Five awards for the best entries for individual datasets in the prior knowledge track.
- One best paper award. This has been attributed based on the IJCNN submissions and the winner will be revealed at the IJCNN workshop.
In addition, deserving challenge participants who need financial support to attend the workshop may send a request to firstname.lastname@example.org.
Important: All final entries must include results on all 5 datasets to facilitate our book-keeping, even if you specialize on a particular dataset in the prior knowledge track. You may use the sample submission to fill in results for datasets you do not want to work on. You must identify yourself with your real name in your final entries.
What is the schedule of the challenge?
The challenge starts October 1st, 2006
Did you publish the intermediate results of the March 1st?
Yes, in addition to the on-line feed-back provided on the validation set, we did 2 intermediate rankings. One in the agnostic track only for the model selection game and one in both both tracks at the original deadline. To avoid compromising the test set too much, we revealed only participant rankings, not the ranking of all the entries, and did not reveal the performances on the test set. The December 1st ranking and the results of the model selection game are available from the NIPS 2006 workshop web page. The results of the March 1st ranking are available from the IJCNN 2007 workshop web page.
How do I participate in the challenge?
Participation to the challenge is free and open to everyone, see the website of the challenge. It is possible to submit on-line results on the validation set during the development period and get immediate performance feed-back. Any time before the end of the challenge, the competitors can return predictions on the final test test. But, the performances on the test set will not be revealed until the challenge is over.
Where are the datasets?
The datasets may be downloaded from the workshop page or the website of the challenge.
What are these "two tracks"?
In this challenge we provide two versions of the datasets:
- The "raw data" for the "prior knowledge" track.
- Preprocessed data for the "agnostic learning track".
You may select either version of the data and return prediction results.
Which track should I choose?
If are not an expert in any of the tasks and you have not entered the previous challenge, you may want to use the agnostic track data first. The data representations are the same as those used in the previous challenge, but the examples and features were re-shuffled. You may then be compelled to improve your performance by taking a look at the raw data.
But, if you are an expert in one of the tasks, you may want to try your proven methods on that data first. Perhaps you beat already badly the agnostic track entries!
How do I select the track I chose?
Just submit results using the data version you chose. We will figure out automatically which version you used (the data splits are the same, but the patterns are shuffled differently in each set).
Can I make mixed submissions?
You may make mixed submissions, with results on "Agnostic learning track" data for some datasets and on "Prior knowledge track" data for others. In this way, you can enter the challenge even if you do not have domain knowledge for some of the tasks. Mixed entries will count towards the "Prior knowledge track". Your entry will count towards the "Agnostic learning track" only if it uses the data provided for that track.
kind of prior knowledge can I use in the "agnostic track"?
you say the there is no prior knowledge in the "Agnostic track data" you
We cannot really prevent participants to do so. The only really agnostic learning results were obtained as part of the previous challenge, which used the same datasets (but a different data shuffling). Then, the participants had no knowledge of the nature of the tasks at hand. This time, the agnostic track people can take a peak at the information provided to the prior knowledge track. We have to rely on the participants good faith: We expect that they honestly disclose in their fact sheet and paper what they did to the data. Eventually, if it is judged by the organizers that prior knowledge was used to improve performance on data provided for the agnostic track, the entry will count towards the "prior knowledge" track.
Is it permitted to use extra training data, which is not provided?
Yes, but only if you enter the prior knowledge track. For instance, the GINA task is a handwritten digit recognition task. You may use extra training data if you have some available. HOWEVER, in no event is it permitted to train on test data. Since we have revealed the original source of the data we use in the challenge and those data are publicly available, it is of course possible to get the test patterns to train. This would be considered cheating, would invalidate the entry and disqualify the entrant. Check to make sure you are not using those data.
Can I vary the number of examples to demonstrate the value of my method?
Absolutely, this is a great idea. Sometimes, when enough data is available, there is not much value added by prior knowledge. But with smaller datasets, there is. Report your results in your paper to increase your chances to win the best paper award.
What will happen to me if I cheat?
Probably nothing. A lot of cheaters never get caught, isn't it? HOWEVER, if you are one of the top ranking entrants, we will spend quite a bit of effort to try to reproduce your results (and other people will). If your results cannot be reproduced, this will be highly suspicious and shed doubt on your integrity... Think about it before you cheat, you may end up not looking so good after all.
How will you proceed to reproduce the results of the winners?
If the methods are published with sufficient details, we may try to re-code it. If this does not work, we will ask the participants to send us their code. We will ask for both the source code and an executable. For commercially available software released prior the start of the challenge, we may accept to gain access only to the executable, provided that the date of release can be verified.
Why is there no feature number in the raw data of HIVA and NOVA?
Because the raw data does not come as a data table. The features must yet be extracted either rom the chemical structure or from raw text.
Why are there sometimes more features in the "agnostic" data?
Because some features that are categorical are encoded as a longer binary code, for convenience. In some cases, distractor features have been introduced in the "agnostic" data. See the data documentation for details.
What is the data format?
For the agnostic track, the data sets are in the same format and include 5 files in text format:
dataname.param: Parameters and statistics about the data
dataname_train.data: Training set (a sparse or a regular matrix, patterns in lines, features in columns).
dataname_valid.data: Development test set or "validation set".
dataname_test.data: Test set.
dataname_train.labels: Labels (truth values of the classes) for training examples.
The matrix data formats used are (in all cases, each line represents a pattern):
- For regular matrices: a space delimited file with a new-line character at the end of each line.
- For sparse matrices with binary values: for each line of the matrix, a space delimited list of indices of the non-zero values. A new-line character at the end of each line.
For the prior knowledge track, the files: dataname.param, and dataname_train.labels are also provided. As additional "prior knowledge", a file dataname_train.mlabels containing the original multi-class labels is provided (THESE SHOULD NOT BE USED AS TRUTH VALUES, the target values are provided by dataname_train.labels). The .data files containing the patterns are in miscellanous formats, depending on the nature of the data:
ADA: Coma separated files (ada_train.csv, ada_valid.csv, and ada_test.csv). Each line represents a feature set. The features are given in ada.feat.
GINA: The regular matrix format (gina_train.data, gina_valid.data, and gina_test.data). Each line is a vector of 28x28 image pixels (the lines have been concatenated).
HIVA: The 3D molecular structure is represented in the MDL-SD format, records beeing separated by $$$$ (hiva_train.sd, hiva_valid.sd, and hiva_test.sd).
NOVA: The data consists of emails, records beeing separated by $$$$ (hiva_train.txt, hiva_valid.txt, and hiva_test.txt).
SYLVA: The regular matrix format (sylva_train.data, sylva_valid.data, and sylva_test.data). The features are given in sylva.feat.
How should I format and submit my results?
The results on each dataset should be formatted in 6 ASCII files:
dataname_train.resu: +-1 classifier outputs for training examples (mandatory for final submissions).
dataname_valid.resu: +-1 classifier outputs for validation examples (mandatory for development and final submissions).
dataname_test.resu: +-1 classifier outputs for test examples (mandatory for final submissions).
dataname_train.conf: Confidence values for training examples (optional).
dataname_valid.conf: Confidence values for validation examples (optional).
dataname_test.conf: Confidence values for test examples (optional).
Format for classifier outputs:
- All .resu files should have one +-1 integer value per line indicating the prediction for the various patterns.
- All .conf files should have one decimal positive numeric value per line indicating classification confidence. The confidence values can be the absolute discriminant values. They do not need to be normalized to look like probabilities. Optionally they can be normalized between 0 and 1 to be interpreted as abs(P(y=1|x)-P(y=-1|x)). They will be used to compute ROC curves and Area Under such Curve (AUC). and other performance metrics such as the negative cross-entropy.
Create a .zip or .tar.gz archive with your files and give to the archive the name of your submission. You may want to check the example submission file zarbi.zip. Matlab code is available to help you format the results.
Submit the results on-line. If any problem, contact the challenge web site administrator.
Is there code to help me read the data and format the results?
Yes: Matlab code is provided for that purpose, see the challenge website. A subset of the full package containing sample code to read the data and format the results can also be downloaded.
there a limit to the number of submissions?
do we need to enter results on all five tasks?
are there no multiclass and regression tasks?
do you have an "agnostic track" in parallel with the "prior knowledge
Is there code I can use to perform the challenge
What is the scoring method?
The final ranking will be based on the average rank of the participants over all 5 datasets, using for each participant his/her best entry on each dataset. This prevents overweighing the datasets with largest error rates.
the results be published?
I use an alias or a funky email not to reveal my identity?
Do I need to let you know what my method is?
me or my group make multiple submissions?
I use a robot to make submissions?
is the difference between a development submission and a final submission?
I attend the workshop if I do not participate to the challenge?
I use the models provided for the challenge?
did you split the data into training, validation, and test set?
motivates the proportion of the data split?
the training, validation, and test set distributed differently?
data split the same in both tracks?
it allowed to use the validation and test sets as extra unlabelled training
The winner of the challenge may not be one of the challenge organizers. However, other workshop organizers that did not participate to the organization of the challenge may enter the competition. The challenge organizers will enter development submissions from time to time to challenge others, under the name "Reference". Reference entries are shown for information only and are not part of the competition.
Can a participant give an arbitrary hard time to the organizers?
DISCLAIMER: ALL INFORMATION, SOFTWARE, DOCUMENTATION, AND DATA ARE PROVIDED "AS-IS". ISABELLE GUYON AND/OR OTHER ORGANIZERS DISCLAIM ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR ANY PARTICULAR PURPOSE, AND THE WARRANTY OF NON-INFRIGEMENT OF ANY THIRD PARTY'S INTELLECTUAL PROPERTY RIGHTS. IN NO EVENT SHALL ISABELLE GUYON AND/OR OTHER ORGANIZERS BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF SOFTWARE, DOCUMENTS, MATERIALS, PUBLICATIONS, OR INFORMATION MADE AVAILABLE FOR THE CHALLENGE.
Who can I ask for more help?
For all other questions, email email@example.com.
Last updated: September