Listar por autoría "86b3c397-575e-467b-91c7-a87b873ff1e1"
Mostrando ítems 1-15 de 15
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A hybrid method to face class overlap and class imbalance on neural networks and multi-class scenarios
Elsevier (2013)Class imbalance and class overlap are two of the major problems in data mining and machine learning. Several studies have shown that these data complexities may affect the performance or behavior of artificial neural ... -
A New Under-Sampling Method to Face Class Overlap and Imbalance
MDPI (2020-07-07)Class overlap and class imbalance are two data complexities that challenge the design of effective classifiers in Pattern Recognition and Data Mining as they may cause a significant loss in performance. Several solutions ...
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A rejection option for the multilayer perceptron using hyperplanes
(2011)Currently, a growing quantity of the Artificial Intelligence tasks demand a high efficiency of the classification systems (classifiers); making an error in the classification of an object or event can cause serious problems. ... -
Associative learning on imbalanced environments: An empirical study
Elsevier (2015-10)Associative memories have emerged as a powerful computational neural network model for several pattern classification problems. Like most traditional classifiers, these models assume that the classes share similar prior ...
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Back propagation with balanced MSE cost Function and nearest neighbor editing for handling class overlap and class imbalance
Springer (2011)The class imbalance problem has been considered a critical factor for designing and constructing the supervised classifiers. In the case of artificial neural networks, this complexity negatively affects the generalization ...
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Combining Multiple Classifiers with Dynamic Weighted Voting
Springer Berlin Heidelberg (2009)When a multiple classifier system is employed, one of the most popular methods to accomplish the classifier fusion is the simple majority voting. However, when the performance of the ensemble members is not uniform, the ...
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DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem
Elsevier (2020-11-12)The class imbalance problem occurs when one class far outnumbers the other classes, causing most traditional classifiers perform poorly on the minority classes. To tackle this problem, a plethora of techniques have been ...
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Edited Nearest Neighbor Rule for Improving Neural Networks Classifications
Springer Berlin Heidelberg (2010)The quality and size of the training data sets is a critical stage on the ability of the artificial neural networks to generalize the characteristics of the training examples. Several approaches are focused to form training ...
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Equilibrating the recognition of the minority Class in the imbalance context
Naturals Publishing (2014-01)In pattern recognition, it is well known that the classifier performance depends on the classification rule and the complexities presented in the data sets (such as class overlapping, class imbalance, outliers, high-dimensional ...
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Estudio empírico del enfoque asociativo en el contexto de los problemas de clasificación
Centro de Investigacion en Computacion (CIC) del Instituto Politecnico Nacional (IPN) (2019)Research carried out by the scientific community has shown that the performance of the classifiers depends not only on the learning rule, if not also on the complexities inherent in the data sets. Some traditional classifiers ...
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Identification of Latent Topics in Patients Surviving COVID-19 in Mexico
IEEE (2023-01-11)With the outbreak of the SARS-CoV-2 o COVID-19 pandemic, multiple studies of risk factors and their influence on patient deaths have been developed. However, little attention is often paid to analyzing patients in risk ...
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Resampling methods versus cost functions for training an MLP in the class imbalance context
Springer-Verlag (2011)The class imbalance problem has been studied from different approaches, some of the most popular are based on resizing the data set or internally basing the discrimination-based process. Both methods try to compensate the ...
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Use of ensemble based on GA for imbalance problem
Springer Verlag (Germany) (2009)In real-world applications, it has been observed that class imbalance (significant differences in class prior probabilities) may produce an important deterioration of the classifier performance, in particular with patterns ...
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Using hybrid associative classifier with translation (HACT) for studying imbalanced data sets
Universidad Nacional de Colombia (Sede Bogotá). Facultad de Ingeniería (2012)En diversos problemas de reconocimiento de patrones, se ha observado que el desequilibrio de clases puede disminuir el desempeño del clasificador, principalmente en los patrones de las clases minoritarias. Una estrategia ...
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Weighted Complete Graphs for Condensing Data
Elsevier B.V. (2020-12-01)In many real-world problems (such as industrial applications, chemistry models, social network analysis, among others), their solution can be obtained by transforming the problem in terms of vertices and edges, that is ...
