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A block-based support vector machine approach to the protein homology prediction task in KDD Cup 2004

This paper describes our solution for the protein homology prediction task in KDD Cup 2004 competition. This task is modeled as a supervised learning problem with multiple performance metrics. Several key characteristics make the problem both novel and challenging, including the concept of data blocks and the presence of large-scale and imbalanced training data. These features make a naive application of the traditional classification algorithms infeasible. Our approach focuses on making full use of the abundant information within the blocks, and developing a new technique for reducing and balancing training data to make the support vector machine applicable to this kind of large-scale and imbalanced learning tasks.