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» Is Combining Classifiers Better than Selecting the Best One
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KDD
2005
ACM
161views Data Mining» more  KDD 2005»
14 years 8 months ago
Combining email models for false positive reduction
Machine learning and data mining can be effectively used to model, classify and discover interesting information for a wide variety of data including email. The Email Mining Toolk...
Shlomo Hershkop, Salvatore J. Stolfo
CIKM
2006
Springer
13 years 11 months ago
Multi-evidence, multi-criteria, lazy associative document classification
We present a novel approach for classifying documents that combines different pieces of evidence (e.g., textual features of documents, links, and citations) transparently, through...
Adriano Veloso, Wagner Meira Jr., Marco Cristo, Ma...
ICNC
2005
Springer
14 years 1 months ago
An Improved Method of Feature Selection Based on Concept Attributes in Text Classification
The feature selection and weighting are two important parts of automatic text classification. In this paper we give a new method based on concept attributes. We use the DEF Terms o...
Shasha Liao, Minghu Jiang
BMCBI
2007
112views more  BMCBI 2007»
13 years 7 months ago
Selecting dissimilar genes for multi-class classification, an application in cancer subtyping
Background: Gene expression microarray is a powerful technology for genetic profiling diseases and their associated treatments. Such a process involves a key step of biomarker ide...
Zhipeng Cai, Randy Goebel, Mohammad R. Salavatipou...
CVPR
2005
IEEE
14 years 1 months ago
Jensen-Shannon Boosting Learning for Object Recognition
In this paper, we propose a novel learning method, called Jensen-Shannon Boosting (JSBoost) and demonstrate its application to object recognition. JSBoost incorporates Jensen-Shan...
Xiangsheng Huang, Stan Z. Li, Yangsheng Wang