Title :
Noise Modeling with Associative Corruption Rules
Author :
Zhang, Yan ; Wu, Xindong
Author_Institution :
Univ. of Vermont, Burlington
Abstract :
This paper presents an active learning approach to the problem of systematic noise inference and noise elimination, specifically the inference of Associated Corruption (AC) rules. AC rules are defined to simulate a common noise formation process in real-world data, in which the occurrence of an error on one attribute is dependent on several other attribute values. Our approach consists of two algorithms, Associative Corruption Forward (ACF) and Associative Corruption Backward (ACB). Algorithm ACF is proposed for noise inference, and ACB is designed for noise elimination. The experimental results show that the ACF algorithm can infer the noise formation correctly, and ACB indeed enhances the data quality for supervised learning.
Keywords :
data mining; inference mechanisms; learning (artificial intelligence); active learning approach; associative corruption backward; associative corruption forward; associative corruption rules; data quality; noise elimination; noise formation process; noise modeling; supervised learning; systematic noise inference; Active noise reduction; Computer science; Data mining; Data preprocessing; Data privacy; Inference algorithms; Noise figure; Noise robustness; Supervised learning; USA Councils;
Conference_Titel :
Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on
Conference_Location :
Omaha, NE
Print_ISBN :
978-0-7695-3018-5
DOI :
10.1109/ICDM.2007.28
