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ISCI
2008
166views more  ISCI 2008»
13 years 5 months ago
A discretization algorithm based on Class-Attribute Contingency Coefficient
Discretization algorithms have played an important role in data mining and knowledge discovery. They not only produce a concise summarization of continuous attributes to help the ...
Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang
AIR
2004
113views more  AIR 2004»
13 years 5 months ago
Class Noise vs. Attribute Noise: A Quantitative Study
Real-world data is never perfect and can often suffer from corruptions (noise) that may impact interpretations of the data, models created from the data and decisions made based on...
Xingquan Zhu, Xindong Wu
SBBD
2004
119views Database» more  SBBD 2004»
13 years 6 months ago
Computing the Dependency Basis for Nested List Attributes
Multi-valued dependencies (MVDs) are an important class of constraints that is fundamental for relational database design. Although modern applications increasingly require the su...
Sven Hartmann, Sebastian Link
TNN
2008
105views more  TNN 2008»
13 years 5 months ago
Incremental Learning of Chunk Data for Online Pattern Classification Systems
This paper presents a pattern classification system in which feature extraction and classifier learning are simultaneously carried out not only online but also in one pass where tr...
Seiichi Ozawa, Shaoning Pang, Nikola K. Kasabov
AUSAI
2006
Springer
13 years 9 months ago
Voting Massive Collections of Bayesian Network Classifiers for Data Streams
Abstract. We present a new method for voting exponential (in the number of attributes) size sets of Bayesian classifiers in polynomial time with polynomial memory requirements. Tra...
Remco R. Bouckaert