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Learning similarity metric to improve the performance of lazy multi-label ranking algorithms | IEEE Conference Publication | IEEE Xplore

Learning similarity metric to improve the performance of lazy multi-label ranking algorithms


Abstract:

The definition of similarity metrics is one of the most important tasks in the development of nearest neighbours and instance based learning methods. Furthermore, the per...Show More

Abstract:

The definition of similarity metrics is one of the most important tasks in the development of nearest neighbours and instance based learning methods. Furthermore, the performance of lazy algorithms can be significantly improved with the use of an appropriate weight vector. In the last years, the learning from multi-label data has attracted significant attention from a lot of researchers, motivated from an increasing number of modern applications that contain this type of data. This paper presents a new method for feature weighting, defining a similarity metric as heuristic to estimate the feature weights, and improving the performance of lazy multi-label ranking algorithms. The experimental stage shows the effectiveness of our proposal.
Date of Conference: 27-29 November 2012
Date Added to IEEE Xplore: 24 January 2013
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Conference Location: Kochi, India

References

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