Visualitza LSI_Articles per autoria "fcb67833-6aaa-4497-9282-a69a652e75dc"
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A Comparative Study of Simple Online Learning Strategies for Streaming Data
World Scientific and Engineering Academy and Society (WSEAS) (2008)Since several years ago, the analysis of data streams has attracted considerably the attention in various research fields, such as databases systems and data mining. The continuous increase in volume of data and the ... -
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 ...
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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 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. ... -
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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An insight into the experimental design for credit risk and corporate bankruptcy prediction systems
Springer (2015-02)Over the last years, it has been observed an increasing interest of the finance and business communities in any application tool related to the prediction of credit and bankruptcy risk, probably due to the need of more ...
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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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Classification of high dimensional and imbalanced hyperspectral imagery data
Springer (2011)The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA is ...
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Cluster validation using information stability measures
Elsevier (2010)In this work, a novel technique to address the problem of cluster validation based on cluster stability properties is presented. The stability index here proposed is based on the variation on some information measures over ...
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Data Reduction Method for Categorical Data Clustering
Springer Verlag (2008)Categorical data clustering constitutes an important part of data mining; its relevance has recently drawn attention from several researchers. As a step in data mining, however, clustering encounters the problem of large ...
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Exploring early classification strategies of streaming data with delayed attributes
Springer Verlag (Germany) (2009)In contrast to traditional machine learning algorithms, where all data are available in batch mode, the new paradigm of streaming data poses additional difficulties, since data samples arrive in a sequence and many hard ...
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Exploring synergetic effects of dimensionality reduction and resampling tools on hyperspectral imagery data classification
Springer (2011)The present paper addresses the problem of the classification of hyperspectral images with multiple imbalanced classes and very high dimensionality. Class imbalance is handled by resampling the data set, whereas PCA and a ...
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Exploring the performance of resampling strategies for the class imbalance problem
Springer Verlag (2010)The present paper studies the influence of two distinct factors on the performance of some resampling strategies for handling imbalanced data sets. In particular, we focus on the nature of the classifier used, along with ...
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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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Human recognition based on gait poses
Springer (2011)This paper introduces a new approach for gait analysis based on the Gait Energy Image (GEI). The main idea is to segment the gait cycle into some biomechanical poses, and to compute a particular GEI for eachpose. Pose-based ...
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Index of balanced accuracy: a performance measure for skewed class distributions
Springer Verlag (Germany) (2009)This paper introduces a new metric, named Index of Balanced Accuracy, for evaluating learning processes in two-class imbalanced domains. The method combines an unbiased index of its overall accuracy and a measure about how ...
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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. ...
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On-line classification of data streams with missing values based on reinforcement learning
Springer (2011)In some applications, data arrive sequentially and they are not available in batch form, what makes difficult the use of traditional classification systems. In addition, some attributes may lack due to some real-world ...
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On-line learning from streaming data with delayed attributes: A comparison of classifiers and strategies
Springer (2011-10)In many real applications, data are not all available at the same time, or it is not affordable to process them all in a batch process, but rather, instances arrive sequentially in a stream. The scenario of streaming data ...
