by Yi Sun, Mark Robinson, Rod Adams, Paul Kaye, Alistair G. Rust, Neil Davey
http://homepages.feis.herts.ac.uk/~nngroup/pubs/papers/Sun-ICMI05.pdf
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Abstract:
Abstract — Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be used in conjunction to improve the quality of predictions. In previous work, we have applied single layer networks, rules sets and support vector machines on predictions from ¡£ ¢ key real valued algorithms. Furthermore, we used a ‘window ’ of consecutive results in the input vector in order to contextualise the neighbouring results. In this paper, we improve the classification result with the aid of a hybrid Adaboost algorithm working on the dataset with windowed inputs. In the proposed algorithm, we first apply weighted majority voting. Those data points which cannot be classified ‘easily ’ using weighted majority voting are then classified using the Adaboost algorithm. We find that our method outperforms each of the original individual algorithms and the other classifiers used previously in this work. I.
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