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CIKM
2008
Springer
15 years 3 months ago
Error-driven generalist+experts (edge): a multi-stage ensemble framework for text categorization
We introduce a multi-stage ensemble framework, ErrorDriven Generalist+Expert or Edge, for improved classification on large-scale text categorization problems. Edge first trains a ...
Jian Huang 0002, Omid Madani, C. Lee Giles
126
Voted
JMLR
2010
161views more  JMLR 2010»
14 years 8 months ago
Feature Selection for Text Classification Based on Gini Coefficient of Inequality
A number of feature selection mechanisms have been explored in text categorization, among which mutual information, information gain and chi-square are considered most effective. ...
Sanasam Ranbir Singh, Hema A. Murthy, Timothy A. G...
IJDLS
2010
108views more  IJDLS 2010»
14 years 10 months ago
Sampling the Web as Training Data for Text Classification
Data acquisition is a major concern in text classification. The excessive human efforts required by conventional methods to build up quality training collection might not always b...
Wei-Yen Day, Chun-Yi Chi, Ruey-Cheng Chen, Pu-Jen ...
149
Voted
ICML
1998
IEEE
16 years 2 months ago
Employing EM and Pool-Based Active Learning for Text Classification
This paper shows how a text classifier's need for labeled training documents can be reduced by taking advantage of a large pool of unlabeled documents. We modify the Query-by...
Andrew McCallum, Kamal Nigam
BMCBI
2008
114views more  BMCBI 2008»
15 years 1 months ago
Combining classifiers for improved classification of proteins from sequence or structure
Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there...
Iain Melvin, Jason Weston, Christina S. Leslie, Wi...