Improving Medical/Biological Data Classification Performance by Wavelet Preprocessing

Authors:
Qi Li;Tao Li;Shenghuo Zhu;Chandra Kambhamettu
Affiliations:
-;-;-;-
Venue:
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Year:
2002

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Abstract

Many real-world datasets contain noise and noisecould degrade the performances of learning algorithms.Motivated from the success of wavelet denoisingtechniques in image data, we explore a generalsolution to alleviate the effect of noisy databy wavelet preprocessing for medical/biological dataclassification. Our experiments are divided into twocategories: one is of different classification algorithmson a specific database (Ecoli [6]) and the other isof a specific classification algorithm (decision tree)on different databases. The experiment results showthat the wavelet denoising of noisy data is able to improvethe accuracies of those classification methods,if the localities of the attributes are strong enough.