Statistical analysis with missing data
Statistical analysis with missing data
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Artificial Intelligence
Robust Learning with Missing Data
Machine Learning
Sequential Model Criticism in Probabilistic Expert Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Artificial Intelligence in Medicine
Environmental Modelling & Software
A survey of the theory of coherent lower previsions
International Journal of Approximate Reasoning
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
The Journal of Machine Learning Research
International Journal of Approximate Reasoning
Hybrid Bayesian network classifiers: Application to species distribution models
Environmental Modelling & Software
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Classifiers that aim at doing credible predictions should rely on carefully elicited prior knowledge. Often this is not available so they should start learning from data in condition of near-ignorance. This paper shows empirically, on an agricultural data set, that established methods of classification do not always adhere to this principle. Traditional ways to represent prior ignorance are shown to have an overwhelming weight compared to the information in the data, producing overconfident predictions. This point is crucial for problems, such as environmental ones, where prior knowledge is often scarce and even the data may not be known precisely. Credal classification, and in particular the naive credal classifier, is proposed as more faithful ways to cope with the ignorance problem. With credal classification, conditions of ignorance may limit the power of the inferences, not the credibility of the predictions.