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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
openaire +2 more sources
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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Data Mining on Imbalanced Data Sets
2008 International Conference on Advanced Computer Theory and Engineering, 2008The majority of machine learning algorithms previously designed usually assume that their training sets are well-balanced, and implicitly assume that all misclassification errors cost equally. But data in real-world is usually imbalanced. The class imbalance problem is pervasive and ubiquitous, causing trouble to a large segment of the data mining ...
Qiong Gu, Zhihua Cai, Li Zhu, Bo Huang
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Imbalanced Multi-instance Data
2016Class imbalance is widely studied in single-instance learning and refers to the situation where the data observations are unevenly distributed among the possible classes. This phenomenon can present itself in MIL as well. Section 9.1 presents a general introduction to the topic of class imbalance, list the types of solutions to deal with it, and the ...
Francisco Herrera +6 more
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Imbalanced Data Preprocessing for Big Data
2020The negative impact on learning associated with imbalanced proportion of classes has exploded lately with the exponential growth of “cheap” data. Many real-world problems present scarce number of instances in one class whereas in others their cardinality is several factors greater.
Julián Luengo +4 more
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