Results 291 to 300 of about 224,004 (328)

IIvotes ensemble for imbalanced data

Intelligent Data Analysis, 2012
In the paper we present IIvotes – a new framework for constructing an ensemble of classifiers from imbalanced data. IIvotes incorporates the SPIDER method for selective data pre-processing into the adaptive Ivotes ensemble. Such an integration is aimed at improving balance between sensitivity and specificity (evaluated by the G-mean measure) for the ...
Błaszczyński, Jerzy, Deckert, Magdalena, Stefanowski, Jerzy, Wilk, Szymon   +3 more
openaire   +1 more source

Classifying Severely Imbalanced Data

2011
Learning from data with severe class imbalance is difficult. Established solutions include: under-sampling, adjusting classification threshold, and using an ensemble. We examine the performance of combining these solutions to balance the sensitivity and specificity for binary classifications, and to reduce the MSE score for probability estimation.
William Klement, Szymon Wilk, Wojtek Michalowski, Stan Matwin   +3 more
openaire   +1 more source

Imbalanced Data Learning

2011
An imbalanced training dataset can pose serious problems for many real-world data-mining tasks that conduct supervised learning. In this chapter,\(^\dagger\) we present a kernel-boundary-alignment algorithm, which considers training-data imbalance as prior information to augment SVMs to improve class-prediction accuracy.
openaire   +1 more source

Credal Clustering for Imbalanced Data

2021
Traditional evidential clustering tends to build clusters where the number of data for each cluster fairly close to each other. However, it may not be suitable for imbalanced data. This paper proposes a new method, called credal clustering (CClu), to deal with imbalanced data based on the theory of belief functions.
Zhang, Zuowei, Liu, Zhunga, Zhou, Kuang, Martin, Arnaud, Zhang, Yiru   +4 more
openaire   +2 more sources

Learning in imbalanced relational data

2008 19th International Conference on Pattern Recognition, 2008
Traditional learning techniques learn from flat data files with the assumption that each class has a similar number of examples. However, the majority of real-world data are stored as relational systems with imbalanced data distribution, where one class of data is over-represented as compared with other classes.
Amal S. Ghanem, Svetha Venkatesh, Geoff West   +2 more
openaire   +1 more source

Imbalanced Data and Resampling Techniques

2021
The SPSS Modeler helps us to build statistical models to predict certain variables. These variables can be that, e.g., a customer buys a product or not or a patient is sick or healthy. Here we have a binary target variable. So far, we discussed methods to predict these variables based on the assumption that the frequency of each possible value is ...
Tilo Wendler, Sören Gröttrup
openaire   +1 more source

Handling Imbalanced Data: A Survey

2017
Nowadays, handling of the imbalance data is a major challenge. Imbalanced data set means the instances of one class are much more than the instances of another class where the majority and minority class or classes are taken as negative and positive, respectively.
Neelam Rout, Debahuti Mishra, Manas Kumar Mallick   +2 more
openaire   +1 more source

Imbalanced Extreme Learning Machine for Classification with Imbalanced Data Distributions

2016
Due to its much faster speed and better generalization performance, extreme learning machine (ELM) has attracted many attentions as an effective learning approach. However, ELM rarely involves strategies for imbalanced data distributions which may exist in many fields.
Wendong Xiao, Jie Zhang, Yanjiao Li, Weidong Yang   +3 more
openaire   +1 more source

Hybrid sampling for imbalanced data

2008 IEEE International Conference on Information Reuse and Integration, 2008
Building a classification model on imbalanced datasets can be a challenging endeavor. Models built on data where examples of one class are greatly outnumbered by examples of the other class(es) tend to sacrifice accuracy with respect to the underrepresented class in favor of maximizing the overall classification rate.
Seiffert, Chris, Khoshgoftaar, Taghi M., Van Hulse, Jason   +2 more
openaire   +1 more source

Online learning for imbalanced data

2018
<p>Online learning receives increasing attention due to its efficiency in handling large-scale streaming data. However, imbalanced data raises a big challenge for traditional online algorithms which aim at minimizing the misclassification error rate.
Xiaoxuan Zhang, Tianbao Yang, Padmini Srinivasan, Nick Street, Kasturi Varadarajan, Xun Zhou   +5 more
openaire   +1 more source