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Qi Li Tao Li , Shenghuo Zhu Chandra Kambhamettu Department of CIS Department of CS Department of CIS University of Delaware University of Rochester University of Delaware Newark, DE 19716 Rochester, NY 14620 Newark, DE 19716 [email protected] ftaoli,[email protected] [email protected] Abstra t Many real-world datasets ontain noise and noise ould degrade the performan es of learning algorithms. Motivated from the su ess of wavelet denoising te hniques in image data, we explore a general solution to alleviate the e e t of noisy data by wavelet prepro essing for medi al/biologi al data lassi ation. Our experiments are divided into two ategories: one is of di erent lassi ation algorithms on a spe i database (E oli [6℄) and the other is of a spe i lassi ation algorithm (de ision tree) on di erent databases. The experiment results show that the wavelet denoising of noisy data is able to improve the a ura ies of those lassi ation methods, if the lo alities of the attributes are strong enough.