Listar INIT_Articles por autoría "fcb67833-6aaa-4497-9282-a69a652e75dc"
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A bias correction function for classification performance assessment in two-class imbalanced problems
Elsevier (2014)This paper introduces a framework that allows to mitigate the impact of class imbalance on most scalar performance measures when used to evaluate the behavior of classifiers. Formally, a correction function is defined with ... -
A literature review on the application of evolutionary computing to credit scoring
Palgrave Macmillan (2013)The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical ... -
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 regression model based on the nearest centroid neighborhood
Springer Verlag (2018)The renowned k-nearest neighbor decision rule is widely used for classification tasks, where the label of any new sample is estimated based on a similarity criterion defined by an appropriate distance function. It has also ...
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A survey on uncertainty quantification in deep learning for financial time series prediction
Elsevier Science Direct (2024-01-28)Investors make decisions about buying and selling a financial asset based on available information. The traditional approach in Deep Learning when trying to predict the behavior of an asset is to take a price history, train ...
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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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Deep transfer learning for the recognition of types of face masks as a core measure to prevent the transmission of COVID-19
Elsevier (2022-08)The use of face masks in public places has emerged as one of the most effective non-pharmaceutical measures to lower the spread of COVID-19 infection. This has led to the development of several detection systems for ...
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Detección de ruido en aprendizaje semisupervisado con el uso de flujos de datos
Universidad de Antioquia (2014)A menudo, es necesario construir conjuntos de entrenamiento. Si disponemos solamente de un número reducido de objetos etiquetados y de un conjunto numeroso de objetos no etiquetados, podemos construir el conjunto ...
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Dissimilarity-Based Linear Models for Corporate Bankruptcy Prediction
Springer (2019-03)Bankruptcy prediction has acquired great relevance for financial institutions due to the complexity of global economies and the growing number of corporate failures, especially since the world financial crisis of 2008. In ...
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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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Exploring the behaviour of base classifiers in credit scoring ensembles
Elsevier (2012)Many techniques have been proposed for credit risk assessment, from statistical models to artificial intelligence methods. During the last few years, different approaches to classifier ensembles have successfully been ...
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Exploring the synergetic effects of sample types on the performance of ensembles for credit risk and corporate bankruptcy prediction
Elsevier (2018-07)Credit risk and corporate bankruptcy prediction has widely been studied as a binary classification problem using both advanced statistical and machine learning models. Ensembles of classifiers have demonstrated their ...
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Financial distress prediction using the hybrid associative memory with translation
Elsevier (2016)This paper presents an alternative technique for financial distress prediction systems. The method is based on a type of neural network, which is called hybrid associative memory with translation. While many different ...
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Fuzzy-Based Time Series Forecasting and Modelling: A Bibliometric Analysis
MDPI (2022-07-07)The purpose of this paper is to present the results of a systematic literature review regarding the development of fuzzy-based models for time series forecasting in the period 2017–2021. The study was conducted using a ...
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Gait-based Gender Classification Considering Resampling and Feature Selection
Engineering and Technology Publishing (2013-06)Two intrinsic data characteristics that arise in many domains are the class imbalance and the high dimensionality, which pose new challenges that should be addressed. When using gait for gender classification, benchmarking ...
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Gene selection and disease prediction from gene expression data using a two-stage hetero-associative memory
Springer Verlag (2019)In general, gene expression microarrays consist of a vast number of genes and very few samples, which represents a critical challenge for disease prediction and diagnosis. This paper develops a two-stage algorithm that ...
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Graphical Framework for Categorizing Data Capabilities and Properties of Objects in the Internet of Things
IEEE (2020-01-27)Things are the core of the Internet of Things (IoT) and must be properly characterized according to the different functions they accomplish. Identifying their capabilities and combining them as sets provides a view on the ...
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Mapping microarray gene expression data into dissimilarity spaces for tumor classification
Elsevier (2015-02)Microarray gene expression data sets usually contain a large number of genes, but a small number of samples. In this article, we present a two-stage classification model by combining feature selection with the ...
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On the suitability of resampling techniques for the class imbalance problem in credit scoring
Palgrave Macmillan (2013)In real-life credit scoring applications, the case in which the class of defaulters is under-represented in comparison with the class of non-defaulters is a very common situation, but it has still received little attention. ...
