Results 1 to 10 of about 1,780,327 (266)
A Novel Boolean Kernels Family for Categorical Data [PDF]
Entropy, 2018 Kernel based classifiers, such as SVM, are considered state-of-the-art algorithms and are widely used on many classification tasks. However, this kind of methods are hardly interpretable and for this reason they are often considered as black-box models ...
Mirko Polato +2 more
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Categorical Data Analysis for High-Dimensional Sparse Gene Expression Data [PDF]
BioTech, 2023 Categorical data analysis becomes challenging when high-dimensional sparse covariates are involved, which is often the case for omics data. We introduce a statistical procedure based on multinomial logistic regression analysis for such scenarios ...
Niloufar Dousti Mousavi +2 more
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, 2008 A very brief survey of regression for categorical data. Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest ...
A. Colin Cameron
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Bayesian Gower agreement for categorical data [PDF]
Scientific ReportsIn this work I present two methods for measuring agreement in nominal and ordinal data. The measures, which employ Gower-type distances, are simple, intuitive, and easy to compute for any number of units and any number of coders. Influential units and/or
John Hughes
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, 2006 A very brief survey of regression for categorical data. Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest ...
Cameron, A. Colin
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Multi-Objective Evolutionary Rule-Based Classification with Categorical Data [PDF]
Entropy, 2018 The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be
Fernando Jiménez +4 more
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Categorical Data Analysis Using a Skewed Weibull Regression Model. [PDF]
Entropy (Basel), 2018 In this paper, we present a Weibull link (skewed) model for categorical response data arising from binomial as well as multinomial model. We show that, for such types of categorical data, the most commonly used models (logit, probit and complementary log-
Caron R, Sinha D, Dey DK, Polpo A.
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The VGAM Package for Categorical Data Analysis [PDF]
Journal of Statistical Software, 2010 Classical categorical regression models such as the multinomial logit and proportional odds models are shown to be readily handled by the vector generalized linear and additive model (VGLM/VGAM) framework. Additionally, there are natural extensions, such
Thomas W. Yee
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CDE++: Learning Categorical Data Embedding by Enhancing Heterogeneous Feature Value Coupling Relationships [PDF]
Entropy, 2020 Categorical data are ubiquitous in machine learning tasks, and the representation of categorical data plays an important role in the learning performance.
Bin Dong, Songlei Jian, Ke Zuo
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Missing Data Imputation for Categorical Variables [PDF]
Statistika: Statistics and Economy Journal, 2022 Dealing with missing data is a crucial part of everyday data analysis. The IMIC algorithm is a missing data imputation method that can handle mixed numerical and categorical datasets. However, the categorical data are crucial for this work.
Jaroslav Horníček, Hana Řezanková
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