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CIKM
2011
Springer
12 years 4 months ago
Imbalanced sentiment classification
Various semi-supervised learning methods have been proposed recently to solve the long-standing shortage problem of manually labeled data in sentiment classification. However, mos...
Shoushan Li, Guodong Zhou, Zhongqing Wang, Sophia ...
ICCV
2011
IEEE
12 years 4 months ago
Sparse Dictionary-based Representation and Recognition of Action Attributes
We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective func...
Qiang Qiu, Zhuolin Jiang, Rama Chellappa
CORR
2011
Springer
183views Education» more  CORR 2011»
12 years 8 months ago
Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction
For large, real-world inductive learning problems, the number of training examples often must be limited due to the costs associated with procuring, preparing, and storing the tra...
Foster J. Provost, Gary M. Weiss
LREC
2008
110views Education» more  LREC 2008»
13 years 6 months ago
Cost-Sensitive Learning in Answer Extraction
One problem of data-driven answer extraction in open-domain factoid question answering is that the class distribution of labeled training data is fairly imbalanced. This imbalance...
Michael Wiegand, Jochen L. Leidner, Dietrich Klako...
IFIP12
2008
13 years 6 months ago
A Study with Class Imbalance and Random Sampling for a Decision Tree Learning System
Sampling methods are a direct approach to tackle the problem of class imbalance. These methods sample a data set in order to alter the class distributions. Usually these methods ar...
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Mar...
WSOM
2009
Springer
13 years 9 months ago
Optimal Combination of SOM Search in Best-Matching Units and Map Neighborhood
Abstract. The distribution of a class of objects, such as images depicting a specific topic, can be studied by observing the best-matching units (BMUs) of the objects’ feature v...
Mats Sjöberg, Jorma Laaksonen
AI
2009
Springer
13 years 11 months ago
Cost-Based Sampling of Individual Instances
In many practical domains, misclassification costs can differ greatly and may be represented by class ratios, however, most learning algorithms struggle with skewed class distrib...
William Klement, Peter A. Flach, Nathalie Japkowic...
KDD
2009
ACM
142views Data Mining» more  KDD 2009»
14 years 5 months ago
Quantification and semi-supervised classification methods for handling changes in class distribution
In realistic settings the prevalence of a class may change after a classifier is induced and this will degrade the performance of the classifier. Further complicating this scenari...
Jack Chongjie Xue, Gary M. Weiss