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Binary relevance efficacy for multilabel classification
TLDR
The goal of multilabel (ML) classification is to induce models able to tag objects with the labels that better describe them. Expand
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Learning Nondeterministic Classifiers
TLDR
Nondeterministic classifiers are defined as those allowed to predict more than one class for some entries from an input space. Expand
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Multilabel classifiers with a probabilistic thresholding strategy
TLDR
In multilabel classification tasks the aim is to find hypotheses able to predict, for each instance, a set of classes or labels rather than a single one. Expand
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Clustering people according to their preference criteria
TLDR
A new algorithm to build clusters of people with closely related tastes, and hence people whose preference judgment sets can be merged in order to learn more reliable ranking functions. Expand
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A simple and efficient method for variable ranking according to their usefulness for learning
TLDR
An algorithm devised to rank input variables according to their usefulness in the context of a learning task is presented. Expand
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Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter
In this paper we present a function to predict the carcass weight for beef cattle. The function uses a few zoometric measurements of the animals taken days before the slaughter. For this purpose weExpand
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Using machine learning procedures to ascertain the influence of beef carcass profiles on carcass conformation scores.
In this study, a total of 163 young-bull carcasses belonging to seven Spanish native beef cattle breeds showing substantial carcass variation were photographed in order to obtain digital assessmentsExpand
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Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option
TLDR
We study the prediction of the risk of developing a disease given genome-wide genotypic data using classifiers with a reject option, which only make a prediction when they are sufficiently certain, but in doubtful situations may reject making a classification. Expand
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Feature subset selection for learning preferences: a case study
TLDR
We develop a method based on Recursive Feature Elimination (RFE) that employs an adaptation of a metric based method devised for model selection (ADJ) to learn the preferences of the experts when they order small groups of animals. Expand
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The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry
In this paper we advocate the application of Artificial Intelligence techniques to quality assessment of food products. Machine Learning algorithms can help us to: (a) extract operative humanExpand
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