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Online Imbalanced Learning with Kernels

by Haiqin Yang, Junjie Hu, Michael R. Lyu, Irwin King
"... Imbalanced learning, or learning from imbalanced data, is a challenging problem in both academy and industry. Nowadays, the streaming imbalanced data become popular and trigger the volume, velocity, and variety issues of learning from these data. To tackle these issues, online learning algorithms ar ..."
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Imbalanced learning, or learning from imbalanced data, is a challenging problem in both academy and industry. Nowadays, the streaming imbalanced data become popular and trigger the volume, velocity, and variety issues of learning from these data. To tackle these issues, online learning algorithms

C4.5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure

by Nitesh V. Chawla - In Proceedings of the ICML’03 Workshop on Class Imbalances , 2003
"... Imbalanced data sets are becoming ubiquitous, as many applications have very few instances of the “interesting ” or “abnormal” class. Traditional machine learning algorithms can be biased towards majority class due to over-prevalence. It is desired that the interesting (minority) class prediction be ..."
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Imbalanced data sets are becoming ubiquitous, as many applications have very few instances of the “interesting ” or “abnormal” class. Traditional machine learning algorithms can be biased towards majority class due to over-prevalence. It is desired that the interesting (minority) class prediction

Imbalanced Datasets using Evolutionary Algorithms

by Satyam Maheshwari, Prof Jitendra Agrawal, Dr. Sanjeev Sharma
"... Abstract — Today’s most of the research interest is in the application of evolutionary algorithms. One of the examples is classification rules in imbalanced domains. The problem of Imbalanced data sets plays a major challenge in data mining community. In imbalanced data sets, the number of instances ..."
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of instances of one class is much higher than the others, and the class of fewer representatives is of more interest from the point of the learning task. Traditional Machine Learning algorithms work well with balanced data sets, but not able to deal with classification of imbalanced data sets. In the present

Investigating the Effect of Sampling Methods for Imbalanced Data Distributions

by Show-jane Yen, Yue-shi Lee, Cheng-han Lin, Jia-ching Ying - Proceedings of IEEE International Conference on Systems, Man, and Cybernetics (SMC'2006 , 2006
"... Abstract-Classification is an important and well-known technique in the field of machine learning, and the training data will significantly influence the classification accuracy. However, the training data in real-world applications often are imbalanced class distribution. It is important to select ..."
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Abstract-Classification is an important and well-known technique in the field of machine learning, and the training data will significantly influence the classification accuracy. However, the training data in real-world applications often are imbalanced class distribution. It is important to select

Cost-Sensitive Learning Methods for Imbalanced Data

by Nguyen Thai-nghe, Zeno Gantner, Lars Schmidt-thieme
"... Abstract — Class imbalance is one of the challenging problems for machine learning algorithms. When learning from highly imbalanced data, most classifiers are overwhelmed by the majority class examples, so the false negative rate is always high. Although researchers have introduced many methods to d ..."
Abstract - Cited by 10 (4 self) - Add to MetaCart
Abstract — Class imbalance is one of the challenging problems for machine learning algorithms. When learning from highly imbalanced data, most classifiers are overwhelmed by the majority class examples, so the false negative rate is always high. Although researchers have introduced many methods

SVMs Modeling for Highly Imbalanced Classification

by Yuchun Tang, Yan-qing Zhang, Nitesh V. Chawla, Sven Krasser , 2009
"... Traditional classification algorithms can be limited in their performance on highly unbalanced data sets. A popular stream of work for countering the problem of class imbalance has been the application of a sundry of sampling strategies. In this correspondence, we focus on designing modifications to ..."
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Traditional classification algorithms can be limited in their performance on highly unbalanced data sets. A popular stream of work for countering the problem of class imbalance has been the application of a sundry of sampling strategies. In this correspondence, we focus on designing modifications

Pruning Support Vectors for Imbalanced Data Classification

by Xue-wen Chen, Byron Gerlach, David Casasent
"... Abstract-- In many practical applications, learning from imbalanced data poses a significant challenge that is increasingly faced by the machine learning community. The class imbalance problem raises issues that are either nonexistent or less severe compared to balanced class cases. This paper prese ..."
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Abstract-- In many practical applications, learning from imbalanced data poses a significant challenge that is increasingly faced by the machine learning community. The class imbalance problem raises issues that are either nonexistent or less severe compared to balanced class cases. This paper

KBA: Kernel boundary alignment considering imbalanced data distribution

by Gang Wu, Edward Y. Chang, Senior Member - IEEE Transactions on Knowledge and Data Engineering (TKDE , 2005
"... Abstract—An imbalanced training data set can pose serious problems for many real-world data mining tasks that employ SVMs to conduct supervised learning. In this paper, we propose a kernel-boundary-alignment algorithm, which considers THE training data imbalance as prior information to augment SVMs ..."
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Abstract—An imbalanced training data set can pose serious problems for many real-world data mining tasks that employ SVMs to conduct supervised learning. In this paper, we propose a kernel-boundary-alignment algorithm, which considers THE training data imbalance as prior information to augment SVMs

Learning and modelling big data

by Barbara Hammer, Haibo He, Thomas Martinetz
"... Abstract. Caused by powerful sensors, advanced digitalisation tech-niques, and dramatically increased storage capabilities, big data in the sense of large or streaming data sets, very high dimensionality, or com-plex data formats constitute one of the major challenges faced by machine learning today ..."
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of existing ones to cope with such situations. The goal of this tutorial is to give an overview about recent machine learning approaches for big data, with a focus on principled algorithmic ideas in the field. 1

Correspondence Imbalanced Learning With a Biased Minimax Probability Machine

by Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu
"... Abstract—Imbalanced learning is a challenged task in machine learning. In this context, the data associated with one class are far fewer than those associated with the other class. Traditional machine learning methods seeking classification accuracy over a full range of instances are not suit-able t ..."
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Abstract—Imbalanced learning is a challenged task in machine learning. In this context, the data associated with one class are far fewer than those associated with the other class. Traditional machine learning methods seeking classification accuracy over a full range of instances are not suit
Results 11 - 20 of 893