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» Ensembles of Multi-instance Learners
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ICMLA
2008
13 years 8 months ago
Ensemble Machine Methods for DNA Binding
We introduce three ensemble machine learning methods for analysis of biological DNA binding by transcription factors (TFs). The goal is to identify both TF target genes and their ...
Yue Fan, Mark A. Kon, Charles DeLisi
AI
2010
Springer
13 years 2 months ago
Robustness of Classifiers to Changing Environments
Abstract. In this paper, we test some of the most commonly used classifiers to identify which ones are the most robust to changing environments. The environment may change over tim...
Houman Abbasian, Chris Drummond, Nathalie Japkowic...
ICDM
2005
IEEE
179views Data Mining» more  ICDM 2005»
14 years 29 days ago
Bagging with Adaptive Costs
Ensemble methods have proved to be highly effective in improving the performance of base learners under most circumstances. In this paper, we propose a new algorithm that combine...
Yi Zhang, W. Nick Street
RSFDGRC
2005
Springer
117views Data Mining» more  RSFDGRC 2005»
14 years 26 days ago
Dependency Bagging
In this paper, a new variant of Bagging named DepenBag is proposed. This algorithm obtains bootstrap samples at first. Then, it employs a causal discoverer to induce from each sam...
Yuan Jiang, Jinjiang Ling, Gang Li, Honghua Dai, Z...
GPEM
2010
134views more  GPEM 2010»
13 years 5 months ago
An ensemble-based evolutionary framework for coping with distributed intrusion detection
A distributed data mining algorithm to improve the detection accuracy when classifying malicious or unauthorized network activity is presented. The algorithm is based on genetic p...
Gianluigi Folino, Clara Pizzuti, Giandomenico Spez...