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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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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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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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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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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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Categorical Embeddings for Tabular Data using PyTorch [PDF]
ITM Web of Conferences, 2023 Deep learning has received much attention for computer vision and natural language processing, but less for tabular data, which is the most prevalent type of data used in industry.
Khedkar Sanskruti +3 more
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Multivariate Regression Forest for Categorical Attribute Data [PDF]
Jisuanji kexue, 2022 As categorical attributes cannot be utilized directly in some regression models like the linear regression,SVR and most multivariate regression trees,a multivariate split method dealing with multiple types of data is prompted in this paper.We define the ...
LIU Zhen-yu, SONG Xiao-ying
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Learning-Based Dissimilarity for Clustering Categorical Data
Applied Sciences, 2021 Comparing data objects is at the heart of machine learning. For continuous data, object dissimilarity is usually taken to be object distance; however, for categorical data, there is no universal agreement, for categories can be ordered in several ...
Edgar Jacob Rivera Rios +3 more
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Categorical data analysis with Practical Application [PDF]
المجلة العراقية للعلوم الاحصائية, 2009 The main purpose of this study is to find out vectors that can be inserted to the statistical analysis of certain groups of variables which are formed as a result of a certain condition depending on categorical data (i.e. Qualitative variable ).
Furat B. Al-Dassy
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