Results 231 to 240 of about 90,025 (264)

An Evaluation of the Robustness of MTS for Imbalanced Data

IEEE Transactions on Knowledge and Data Engineering, 2007
In classification problems, the class imbalance problem will cause a bias on the training of classifiers and will result in the lower sensitivity of detecting the minority class examples. The Mahalanobis-Taguchi System (MTS) is a diagnostic and forecasting technique for multivariate data.
Chao-Ton Su, Yu-Hsiang Hsiao
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A hybrid sampling method for imbalanced data

2015 IEEE 12th International Multi-Conference on Systems, Signals & Devices (SSD15), 2015
With the diversification of applications and the emergence of new trends in challenging applications such as in the computer vision domain, classical machine learning systems usually perform poorly while confronting two common problems: the training data of negative examples, which outnumber the positive ones, and the large intra-class variations ...
Sami Gazzah, Amina Hechkel, Najoua Essoukri Ben Amara   +2 more
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Sequential extraction of clusters for imbalanced data

2013 IEEE International Conference on Granular Computing (GrC), 2013
K-means type clustering has a central role in various clustering algorithms. In spite of its usefulness, there is a well-known drawback, the number of clusters should be determined beforehand, and clustering results are strongly depends of this number.
Hengjin Tang, Sadaaki Miyamoto
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Classifying highly imbalanced ICU data

Health Care Management Science, 2012
Highly imbalanced data sets are those where the class of interest is rare. In this paper, we compare the performance of several common data mining methods, logistic regression, discriminant analysis, Classification and Regression Tree (CART) models, C5, and Support Vector Machines (SVM) in predicting the discharge status (alive or deceased, with ...
Yazan F, Roumani, Jerrold H, May, David P, Strum, Luis G, Vargas   +3 more
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Imbalanced Data for Knowledge Tracing

2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 2023
Jyun-Yi Chen, I-Wei Lai
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Graph classification with imbalanced data sets

The First Asian Conference on Pattern Recognition, 2011
Many graph classification methods have been proposed in recent years. These graph classification methods can perform well with balanced graph data sets, but perform poorly with imbalanced graph data sets. In this paper, we propose a new graph classification method based on cost sensitivity to deal with imbalance. First, we introduce a misclassification
Gang-Song Xiao, Xiao-yun Chen
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Actively Balanced Bagging for Imbalanced Data

2017
Under-sampling extensions of bagging are currently the most accurate ensembles specialized for class imbalanced data. Nevertheless, since improvements of recognition of the minority class, in this type of ensembles, are usually associated with a decrease of recognition of majority classes, we introduce a new, two phase, ensemble called Actively ...
Jerzy Blaszczynski, Jerzy Stefanowski
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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.
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A review of methods for imbalanced multi-label classification

Pattern Recognition, 2021
Adane Nega Tarekegn, Mario Giacobini, Krzysztof Michalak   +2 more
exaly  

Evidential Combination of Classifiers for Imbalanced Data

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022
Jiawei Niu, Zhunga Liu, Yao Lu 0010, Zaidao Wen   +3 more
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