In:
ACM SIGKDD Explorations Newsletter, Association for Computing Machinery (ACM), Vol. 6, No. 2 ( 2004-12), p. 128-131
Abstract:
In this paper we describe the winning model for the performance measure "lowest ranked homologous sequence" (RKL). This was a subtask of the Protein Homology Prediction task of the KDD Cup 2004. The goal was to predict protein homology for different performance metrics. The given data was organized in blocks, each of which corresponds to a specific native sequence. The two metrics average precision (APR) and RKL explicitly make use of this block structure. Our solution consists of two parts. The first one is a global classification SVM not aware of the block structure. The second part is a k-NearestNeighbor scheme for block similarity, used to train ranking SVMs on the fly. Furthermore, we sketch our approach to optimize the root-mean-squared-error and report some alternative solutions that turned out to be suboptimal.
Type of Medium:
Online Resource
ISSN:
1931-0145
,
1931-0153
DOI:
10.1145/1046456.1046477
Language:
English
Publisher:
Association for Computing Machinery (ACM)
Publication Date:
2004
detail.hit.zdb_id:
2082223-6
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