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Credible classification for environmental problems

https://doi.org/10.1016/j.envsoft.2004.10.006
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Building classification trees using the total uncertainty criterion
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Pattern Classification
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Partial Identification of Probability Distributions
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Data Mining: Practical Machine Learning Tools and Techniques
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Robust Bayes classifiers
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3 The selection problem in econometrics and statistics
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On the necessity of using imprecise probabilities for modelling environental systems peter reichert
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Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations
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The naive credal classifier
inferredcites →https://doi.org/10.1016/s0378-3758(01)00206-3openalex-snapshot
Exact credal treatment of missing data
inferredcites →https://doi.org/10.1016/s0933-3657(03)00046-0openalex-snapshot
Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data
inferredcites →https://doi.org/10.1017/s0020268100012270openalex-snapshot
Some observations on inverse probability including a new indifference rule
inferredcites →https://doi.org/10.1023/a:1010968702992openalex-snapshot
Robust Learning with Missing Data
inferredcites →https://doi.org/10.1023/a:1025822321743openalex-snapshot
Tree-Based Credal Networks for Classification
inferredcites →https://doi.org/10.1038/109132a0openalex-snapshot
The Theory of Probability
inferredcites →https://doi.org/10.1109/34.204903openalex-snapshot
Sequential model criticism in probabilistic expert systems
inferredcites →https://doi.org/10.1109/tai.1994.346412openalex-snapshot
MLC++: a machine learning library in C++
inferredcites →https://doi.org/10.1111/j.2517-6161.1996.tb02065.xopenalex-snapshot
Inferences from Multinomial Data: Learning About a Bag of Marbles
inferredcites →https://doi.org/10.1145/507338.507355openalex-snapshot
Data mining
inferredcites →https://doi.org/10.2307/2234664openalex-snapshot
Numerical Recipes in C: The Art of Scientific Computing.
inferredcites →https://doi.org/10.2307/2289883openalex-snapshot
Statistical Analysis With Missing Data
inferredcites →https://doi.org/10.2307/2290362openalex-snapshot
Statistical Reasoning With Imprecise Probabilities
inferredcites →https://doi.org/10.5281/zenodo.19712698openalex-snapshot
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
inferredcites →https://doi.org/10.5860/choice.27-5158openalex-snapshot
Numerical recipes in Pascal: the art of scientific computing

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Incorporating Individual Tree Features into an Area-Based Approach for Estimating Forest Stock Volume
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References
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Bayesian Networks with Imprecise Probabilities: Theory and Application to Classification
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A Multiple Criteria Decision Analysis Model Based on ELECTRE TRI-C for Erosion Risk Assessment in Agricultural Areas
inferred← citeshttps://doi.org/10.1007/s13595-018-0712-zopenalex-snapshot
Modeling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data
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Using general linear model, Bayesian Networks and Naive Bayes classifier for prediction of Karenia selliformis occurrences and blooms
inferred← citeshttps://doi.org/10.1016/j.envsoft.2008.01.004openalex-snapshot
JNCC2: An extension of naive Bayes classifier suited for small and incomplete data sets
inferred← citeshttps://doi.org/10.1016/j.envsoft.2010.04.016openalex-snapshot
Hybrid Bayesian network classifiers: Application to species distribution models
inferred← citeshttps://doi.org/10.1016/j.ijar.2007.12.001openalex-snapshot
A survey of the theory of coherent lower previsions
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A nonparametric predictive alternative to the Imprecise Dirichlet Model: The case of a known number of categories
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Thirty years of credal networks: Specification, algorithms and complexity
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Evolutionary feature selection to estimate forest stand variables using LiDAR
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Using extreme prior probabilities on the Naive Credal Classifier
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A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov–Smirnov bounds
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Building on Probability: Uncertainty and UVaR
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Modelling canopy fuel variables for Pinus radiata D. Don in NW Spain with low-density LiDAR data
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Generalized models for subtropical forest inventory attribute estimations using a rule-based exhaustive combination approach with airborne LiDAR-derived metrics
inferred← citeshttps://doi.org/10.1142/s0218001413510051openalex-snapshot
AN IMPRECISE BOOSTING-LIKE APPROACH TO CLASSIFICATION
inferred← citeshttps://doi.org/10.1613/jair.4355openalex-snapshot
Probabilistic Inference in Credal Networks: New Complexity Results
inferred← citeshttps://doi.org/10.4018/978-1-4666-1806-0.ch005openalex-snapshot
Supervised Classification with Bayesian Networks
inferred← citeshttps://doi.org/10.4995/raet.2019.11241openalex-snapshot
Estimación de la distribución vertical de combustibles finos del dosel de copas en masas de Pinus sylvestris empleando datos LiDAR de baja densidad
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Assessment of hydrological and seasonal controls over the nitrate flushing from a forested watershed using a data mining technique
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Distributionally robust, skeptical inferences in supervised classification using imprecise probabilities
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