The goal of multilabel (ML) classification is to induce models able to tag objects with the labels that better describe them. The main baseline for ML classification is Binary Relevance (BR), which is commonly criticized in the literature because of its label independence assumption. Despite this fact, this paper discusses some interesting properties of BR, mainly that it produces optimal models for several ML loss functions. Additionally, we present an analytical study about ML benchmarks datasets, pointing out some shortcomings. As a result, this paper proposes the use of synthetic datasets to better analyze the behavior of ML methods in domains with different characteristics. To support this claim, we perform some experiments using synthetic data proving the competitive performance of BR with respect to a more complex method in difficult problems with many labels, a conclusion which was not stated by previous studies.

In this paper we tackle a real world problem, the search of a function to evaluate the merits of beef cattle as meat producers. The independent variables represent a set of live animals' measurements; while the outputs cannot be captured with a single number, since the available experts tend to assess each animal in a relative way, comparing animals with the other partners in the same batch. Therefore, this problem can not be solved by means of regression methods; our approach is to learn the preferences of the experts when they order small groups of animals. Thus, the problem can be reduced to a binary classification, and can be dealt with a Support Vector Machine (SVM) improved with the use of a feature subset selection (FSS) method. We develop a method based on Recursive Feature Elimination (RFE) that employs an adaptation of a metric based method devised for model selection (ADJ). Finally, we discuss the extension of the resulting method to more general settings, and provide a comparison with other possible alternatives.

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 human knowledge from a set of examples; (b) conclude interpretable rules for classifying samples regardless of the non-linearity of the human behaviour or process; and (c) help us to ascertain the degree of influence of each objective attribute of the assessed food on the final decision of an expert. We illustrate these topics with an example of how it is possible to clone the behaviour of bovine carcass classifiers, leading to possible further industrial applications. #

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. Some state-of-the-art multilabel learners use a thresholding strategy, which consists in computing a score for each label and then predicting the set of labels whose score is higher than a given threshold. When this score is the estimated posterior probability, the selected threshold is typically 0.5.In this paper we introduce a family of thresholding strategies which take into account the posterior probability of all possible labels to determine a different threshold for each instance. Thus, we exploit some kind of interdependence among labels to compute this threshold, which is optimal regarding a given expected loss function. We found experimentally that these strategies outperform other thresholding options for multilabel classification. They provide an efficient method to implement a learner which considers the interdependence among labels in the sense that the overall performance of the prediction of a set of labels prevails over that of each single label.

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 we have used Artificial Intelligence tools based on Support Vector Machines for Regression (SVR). We report a case study done with a set of 390 measurements of 144 animals taken from 2 to 222 days in advance of the slaughter. We used animals of the breed Asturiana de los Valles, a specialized beef breed from the North of Spain. The results obtained show that it is possible to predict carcass weights 150 days before the slaughter day with an average absolute error of 4.27% of the true value. The prediction function is a polynomial of degree 3 that uses 5 lengths and the estimation of the round profile of the animals.