Results 251 to 260 of about 222,810 (285)

Design efficiency for imbalanced multilevel data

Behavior Research Methods, 2009
The importance of accurate estimation and of powerful statistical tests is widely recognized but has rarely been acknowledged in practice in the social and behavioral sciences. This is especially true for estimation and testing when one is dealing with multilevel designs, not least because approximating accuracy and power is more complex due to having ...
Wilfried, Cools, Wim, Van den Noortgate, Patrick, Onghena   +2 more
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A fuzzy classifier for imbalanced and noisy data

2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542), 2005
This paper deals with the learning concept in the presence of noise (overlap) and imbalance in the training set. The starting assumption is that recognition of the smaller class is much more important than that of the larger class. A fuzzy classifier capable of achieving this based on the relation between fuzzy sets and probability distributions as ...
Sofia Visa, Anca L. Ralescu
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Introduction to Imbalanced Data

2019
An imbalance of sample sizes among class labels makes it difficult to obtain high classification accuracy in many scientific fields, including medical diagnosis, bioinformatics, biology, and fisheries management. This difficulty is referred to as “class imbalance problem” and is considered to be among the 10 most important problems in data mining ...
Osamu Komori, Shinto Eguchi
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The Influence of Sampling on Imbalanced Data Classification

2019 8th Brazilian Conference on Intelligent Systems (BRACIS), 2019
Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border ...
Victor H. Barella, Luís Paulo F. Garcia, André C. P. L. F. de Carvalho   +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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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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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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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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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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Classification of wine quality with imbalanced data

2016 IEEE International Conference on Industrial Technology (ICIT), 2016
We propose a data analysis approach to classify wine into different quality categories. A data set of white wines of 4898 observations obtained from the Minho region in Portugal was used in our analysis. As the occurrence of events in the data set was imbalanced with about 93% of the observations are from one category, we applied the Synthetic Minority
Gongzhu Hu, Tan Xi, Faraz Mohammed, Huaikou Miao   +3 more
openaire   +1 more source