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ISDA
2010
IEEE
13 years 5 months ago
Comparing SVM ensembles for imbalanced datasets
Real life datasets often suffer from the problem of class imbalance, which thwarts supervised learning process. In such data sets examples of positive (minority) class are signific...
Vasudha Bhatnagar, Manju Bhardwaj, Ashish Mahabal
ICRA
2009
IEEE
158views Robotics» more  ICRA 2009»
13 years 4 months ago
MMM-classification of 3D range data
This paper presents a method for accurately segmenting and classifying 3D range data into particular object classes. Object classification of input images is necessary for applicat...
Anuraag Agrawal, Atsushi Nakazawa, Haruo Takemura
HIS
2008
13 years 8 months ago
Evolutionary Training Set Selection to Optimize C4.5 in Imbalanced Problems
Classification in imbalanced domains is a recent challenge in machine learning. We refer to imbalanced classification when data presents many examples from one class and few from ...
Salvador García, Francisco Herrera
PAKDD
2010
ACM
165views Data Mining» more  PAKDD 2010»
13 years 9 months ago
Classification and Novel Class Detection in Data Streams with Active Mining
We present ActMiner, which addresses four major challenges to data stream classification, namely, infinite length, concept-drift, conceptevolution, and limited labeled data. Most o...
Mohammad M. Masud, Jing Gao, Latifur Khan, Jiawei ...
ECML
2006
Springer
13 years 10 months ago
Active Learning with Irrelevant Examples
Abstract. Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there ma...
Dominic Mazzoni, Kiri Wagstaff, Michael C. Burl