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» Generation of Attributes for Learning Algorithms
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PAKDD
2010
ACM
151views Data Mining» more  PAKDD 2010»
15 years 4 months ago
Ensemble Learning Based on Multi-Task Class Labels
Abstract. It is well known that diversity among component classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods achieve this goal through resam...
Qing Wang, Liang Zhang
CEC
2007
IEEE
15 years 5 months ago
WAIRS: improving classification accuracy by weighting attributes in the AIRS classifier
— AIRS (Artificial Immune Recognition System) has shown itself to be a competitive classifier. It has also proved to be the most popular immune inspired classifier. However, rath...
Andrew Secker, Alex Alves Freitas
CEC
2007
IEEE
15 years 3 months ago
Mining association rules from databases with continuous attributes using genetic network programming
Most association rule mining algorithms make use of discretization algorithms for handling continuous attributes. Discretization is a process of transforming a continuous attribute...
Karla Taboada, Eloy Gonzales, Kaoru Shimada, Shing...
ICDM
2006
IEEE
152views Data Mining» more  ICDM 2006»
15 years 5 months ago
Application of Graph-based Data Mining to Metabolic Pathways
We present a method for finding biologically meaningful patterns on metabolic pathways using the SUBDUE graph-based relational learning system. A huge amount of biological data t...
Chang Hun You, Lawrence B. Holder, Diane J. Cook
NIPS
1997
15 years 18 days ago
Learning Generative Models with the Up-Propagation Algorithm
Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
Jong-Hoon Oh, H. Sebastian Seung