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ICASSP
2011
IEEE
10 years 5 months ago
Online learning with minority class resampling
This paper considers using online binary classification for target detection where the goal is to identify signals of interest within a sequence of received signals generated by ...
Michael J. Pekala, Ashley J. Llorens
ISDA
2010
IEEE
10 years 11 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
2010
IEEE
158views Robotics» more  ICRA 2010»
10 years 12 months ago
Coping with imbalanced training data for improved terrain prediction in autonomous outdoor robot navigation
Abstract— Autonomous robot navigation in unstructured outdoor environments is a challenging and largely unsolved area of active research. The navigation task requires identifying...
Michael J. Procopio, Jane Mulligan, Gregory Z. Gru...
ISCI
2008
124views more  ISCI 2008»
11 years 1 months ago
A weighted rough set based method developed for class imbalance learning
In this paper, we introduce weights into Pawlak rough set model to balance the class distribution of a data set and develop a weighted rough set based method to deal with the clas...
Jinfu Liu, Qinghua Hu, Daren Yu
BMCBI
2010
113views more  BMCBI 2010»
11 years 1 months ago
Class prediction for high-dimensional class-imbalanced data
Background: The goal of class prediction studies is to develop rules to accurately predict the class membership of new samples. The rules are derived using the values of the varia...
Rok Blagus, Lara Lusa
ICPR
2010
IEEE
11 years 1 months ago
The Binormal Assumption on Precision-Recall Curves
—The precision-recall curve (PRC) has become a widespread conceptual basis for assessing classification performance. The curve relates the positive predictive value of a classi...
Kay Henning Brodersen, Cheng Soon Ong, Klaas Enno ...
IFIP12
2008
11 years 2 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...
FLAIRS
2008
11 years 3 months ago
Building Useful Models from Imbalanced Data with Sampling and Boosting
Building useful classification models can be a challenging endeavor, especially when training data is imbalanced. Class imbalance presents a problem when traditional classificatio...
Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van H...
GECCO
2006
Springer
205views Optimization» more  GECCO 2006»
11 years 5 months ago
Bounding XCS's parameters for unbalanced datasets
This paper analyzes the behavior of the XCS classifier system on imbalanced datasets. We show that XCS with standard parameter settings is quite robust to considerable class imbal...
Albert Orriols-Puig, Ester Bernadó-Mansilla
SBIA
2004
Springer
11 years 6 months ago
Learning with Class Skews and Small Disjuncts
One of the main objectives of a Machine Learning – ML – system is to induce a classifier that minimizes classification errors. Two relevant topics in ML are the understanding...
Ronaldo C. Prati, Gustavo E. A. P. A. Batista, Mar...
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