ICASSP
2011
IEEE
12 years 8 months ago
2011
IEEE
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 ...
ISDA
2010
IEEE
13 years 2 months ago
2010
IEEE
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...
ICRA
2010
IEEE
13 years 2 months ago
2010
IEEE
Abstract— Autonomous robot navigation in unstructured outdoor environments is a challenging and largely unsolved area of active research. The navigation task requires identifying...
ISCI
2008
13 years 4 months ago
2008
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...
BMCBI
2010
13 years 4 months ago
2010
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...
ICPR
2010
IEEE
13 years 4 months ago
2010
IEEE
—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...
IFIP12
2008
13 years 5 months ago
2008
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...
FLAIRS
2008
13 years 6 months ago
2008
Building useful classification models can be a challenging endeavor, especially when training data is imbalanced. Class imbalance presents a problem when traditional classificatio...
GECCO
2006
Springer
13 years 8 months ago
2006
Springer
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...
SBIA
2004
Springer
13 years 9 months ago
2004
Springer
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...