Similarity encoding for learning with dirty categorical variables
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DOI: 10.1007/s10994-018-5724-2
Abstract: For statistical learning, categorical variables in a table are usually considered as discrete entities and encoded separately to feature vectors, e.g., with one-hot encoding. “Dirty” non-curated data give rise to categorical variables with a very… read more here.
Keywords: encoding learning; similarity encoding; similarity; categorical variables ... See more keywords
Advanced Method Optimization with Categorical and Constrained Continuous Parameters.
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DOI: 10.1021/acs.analchem.5c02397
Abstract: Traditional approaches to analytical method optimization (e.g., univariate and "guess-and-check") can be time-consuming, costly, and often fail to identify true optima within the parameter space. Previous work defined and implemented a generalized technique for method… read more here.
Keywords: optimization categorical; categorical variables; advanced method; method optimization ... See more keywords
Accuracy and distribution of baseline categorical variables and p-values in spine randomized controlled trials
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DOI: 10.1098/rsos.240170
Abstract: Levayer and colleagues assessed integrity issues in randomized controlled trials (RCTs) in four spine journals using baseline p-values from categorical variables, concluding that there was no evidence of ‘systemic fraudulent behaviour’. We used their published… read more here.
Keywords: baseline values; controlled trials; categorical variables; baseline ... See more keywords
Customized Evolutionary Expensive Optimization: Efficient Search and Surrogate Strategies for Continuous and Categorical Variables
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2024.3519537
Abstract: Surrogate-assisted evolutionary algorithms for addressing expensive optimization problems with both continuous and categorical variables (EOPCCVs) are still in the early stages of development. This study makes significant advancements by leveraging the mixed-variable nature of EOPCCVs… read more here.
Keywords: categorical variables; customized evolutionary; continuous categorical; optimization ... See more keywords
Imputation of incomplete ordinal and nominal data by predictive mean matching
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DOI: 10.1177/09622802251362642
Abstract: Multivariate imputation using chained equations is a popular algorithm for imputing missing data that entails specifying multivariable models through conditional distributions. Two standard imputation methods for imputing missing continuous variables are parametric imputation using a… read more here.
Keywords: mean matching; predictive mean; categorical variables; regression ... See more keywords
New discrimination procedure of location model for handling large categorical variables
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DOI: 10.17576/jsm-2017-4606-20
Abstract: The location model proposed in the past is a predictive discriminant rule that can classify new observations into one of two predefined groups based on mixtures of continuous and categorical variables. The ability of location… read more here.
Keywords: location model; categorical variables; large categorical; model ... See more keywords
Two-Step Clustering for Mineral Prospectivity Mapping: A Case Study from the Northeastern Edge of the Jiaolai Basin, China
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DOI: 10.3390/min14111089
Abstract: The advancement of geological big data has rendered data-driven methodologies increasingly vital in Mineral Prospectivity Mapping. The effective integration of quantitative and qualitative data, including experiential and knowledge-based insights, is crucial in geological data fusion.… read more here.
Keywords: prospectivity mapping; categorical variables; mineral prospectivity; step clustering ... See more keywords