Changes between Version 26 and Version 27 of GSoC/Ideas


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Timestamp:
03/11/11 21:51:06 (4 years ago)
Author:
gw0
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  • GSoC/Ideas

    v26 v27  
    7373Orange lacks support for multi-label learning and classification -- on data-structure, algorithmic and GUI level. Update the structures, implement at least a few algorithms, adapt evaluation methods and add GUI support for it. A neat repository of literature on multi-label learning is ML&KD's web page [http://mlkd.csd.auth.gr/multilabel.html Learning from Multi-Label Data]. 
    7474 
    75 Useful skills: Python. C++. A bit of machine learning. 
     75Useful skills: Python. C/C++. A bit of machine learning. 
    7676 
    7777Level from 1 (beginner) to 5 (professional): 4 
     
    8282With a smarter implementation and/or under special conditions (e.g. Euclidean distance) this can be improved. 
    8383 
    84 Useful skills: Python and C. 
     84Useful skills: Python and C/C++. 
    8585 
    8686Level from 1 (beginner) to 5 (professional): 3 
     87 
     88== Add built-in attribute weighting support == 
     89 
     90Traditionally learning algorithms do not support attribute weighting and treat all attributes as if they were equally important, but this is not the case in real data sets. It is known that variants of algorithms that use this additional data usually perform better. Because Orange at the moment does not include functionality for this kind of meta data, none of the algorithms support it out of the box. It would be great to add support for this (and possibly other) kind of meta data to attributes and other data structures and extend various learning algorithms to use it. For the second part some research of how to add this to each of the algorithm and probably how to generalize the solution will need to be done. 
     91 
     92Useful skills: Python and C/C++. Understanding of machine learning algorithms. 
     93 
     94Level from 1 (beginner) to 5 (professional): 4 
    8795 
    8896== Time-series analysis ==