Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Margin Trees for High-dimensional Classification
The Journal of Machine Learning Research
A Tutorial on Conformal Prediction
The Journal of Machine Learning Research
Learning Reliable Classifiers From Small or Incomplete Data Sets: The Naive Credal Classifier 2
The Journal of Machine Learning Research
Trust Region Newton Method for Logistic Regression
The Journal of Machine Learning Research
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
Uncertainty in clustering and classification
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Viability of an alarm predictor for coffee rust disease using interval regression
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning data structure from classes: A case study applied to population genetics
Information Sciences: an International Journal
Multilabel classifiers with a probabilistic thresholding strategy
Pattern Recognition
Evaluating credal classifiers by utility-discounted predictive accuracy
International Journal of Approximate Reasoning
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Introducing the Discriminative Paraconsistent Machine (DPM)
Information Sciences: an International Journal
Investigating Topic Models' Capabilities in Expression Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A unified view of class-selection with probabilistic classifiers
Pattern Recognition
Credal ensembles of classifiers
Computational Statistics & Data Analysis
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Nondeterministic classifiers are defined as those allowed to predict more than one class for some entries from an input space. Given that the true class should be included in predictions and the number of classes predicted should be as small as possible, these kind of classifiers can be considered as Information Retrieval (IR) procedures. In this paper, we propose a family of IR loss functions to measure the performance of nondeterministic learners. After discussing such measures, we derive an algorithm for learning optimal nondeterministic hypotheses. Given an entry from the input space, the algorithm requires the posterior probabilities to compute the subset of classes with the lowest expected loss. From a general point of view, nondeterministic classifiers provide an improvement in the proportion of predictions that include the true class compared to their deterministic counterparts; the price to be paid for this increase is usually a tiny proportion of predictions with more than one class. The paper includes an extensive experimental study using three deterministic learners to estimate posterior probabilities: a multiclass Support Vector Machine (SVM), a Logistic Regression, and a Naïve Bayes. The data sets considered comprise both UCI multi-class learning tasks and microarray expressions of different kinds of cancer. We successfully compare nondeterministic classifiers with other alternative approaches. Additionally, we shall see how the quality of posterior probabilities (measured by the Brier score) determines the goodness of nondeterministic predictions.