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Enhancing Clinical Predictive Modeling through Model Complexity-Driven Class Proportion Tuning for Class Imbalanced Data: An Empirical Study on Opioid Overdose Prediction. [PDF]
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Classifying highly imbalanced ICU data
Health Care Management Science, 2012Highly 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 +3 more
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Protein classification with imbalanced data
Proteins: Structure, Function, and Bioinformatics, 2007AbstractGenerally, protein classification is a multi‐class classification problem and can be reduced to a set of binary classification problems, where one classifier is designed for each class. The proteins in one class are seen as positive examples while those outside the class are seen as negative examples.
Xing-Ming, Zhao +3 more
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Hybrid Classifier Ensemble for Imbalanced Data
IEEE Transactions on Neural Networks and Learning Systems, 2020The class imbalance problem has become a leading challenge. Although conventional imbalance learning methods are proposed to tackle this problem, they have some limitations: 1) undersampling methods suffer from losing important information and 2) cost-sensitive methods are sensitive to outliers and noise.
Kaixiang Yang +6 more
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2018
A very challenging issue in real world data is that in many domains like medicine, finance, marketing, web, telecommunication, management etc., the distribution of data among classes is inherently imbalanced. A widely accepted researched issue is that the traditional classifier algorithms assume a balanced distribution among the classes. Data imbalance
Lincy Mathews, Seetha Hari
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A very challenging issue in real world data is that in many domains like medicine, finance, marketing, web, telecommunication, management etc., the distribution of data among classes is inherently imbalanced. A widely accepted researched issue is that the traditional classifier algorithms assume a balanced distribution among the classes. Data imbalance
Lincy Mathews, Seetha Hari
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Imbalanced big data classification
Proceedings of the Workshop Program of the 19th International Conference on Distributed Computing and Networking, 2018In the domain of machine learning, quality of data is most critical component for building good models. Predictive analytics is an AI stream used to predict future events based on historical learnings and is used in diverse fields like predicting online frauds, oil slicks, intrusion attacks, credit defaults, prognosis of disease cells etc ...
Avnish Kumar Rastogi +2 more
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IEEE Transactions on Knowledge and Data Engineering, 2009
With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data
null Haibo He, E.A. Garcia
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With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data
null Haibo He, E.A. Garcia
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Introduction to Imbalanced Data
2019An 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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Design efficiency for imbalanced multilevel data
Behavior Research Methods, 2009The 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 +2 more
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