Catálogo de publicaciones - libros

For One-Class Classification problems several methods have been proposed in the literature. These methods all have the common feature that the decision boundary is learnt by just using a set of the positive examples. Here we propose a method that extends the training set with a counter-example set, which is generated directly using the set of positive examples. Using the extended training set, a binary classifier (here -SVM) is applied to separate the positive and the negative points. The results of this novel technique are compared with those of One-Class SVM and the Gaussian Mixture Model on several One-Class Classification tasks.

We report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this approach, we propose to use a new Bayesian decision theoretical framework in which the loss is conditioned with the current decision and the expected decisions after additional features are extracted as well as the consistency among the current and expected decisions. This approach allows us to force the feature extraction for samples for which the current and expected decisions are inconsistent. On the other hand, it forces not to extract any features in the case of consistency, leading to less costly but equally accurate decisions. In this work, we apply this approach to a medical diagnosis problem and demonstrate that it reduces the overall feature extraction cost up to 47.61 percent without decreasing the accuracy.

Action-based dependency parsing, also known as deterministic dependency parsing, has often been regarded as a time efficient parsing algorithm while its parsing accuracy is a little lower than the best results reported by more complex parsing models. In this paper, we compare action-based dependency parsers with complex parsing methods such as all-pairs parsers on Penn Chinese Treebank. For Chinese dependency parsing, action-based parsers outperform all-pairs parsers. But action-based parsers do not compute the probability of the whole dependency tree. They only determine parsing actions stepwisely by a trained classifier. To globally model parsing actions of all steps that are taken on the input sentence, we propose two kinds of probabilistic parsing action models that can compute the probability of the whole dependency tree. Results show that our probabilistic parsing action models perform better than the original action-based parsers, and our best result improves much over them.

We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm initially proposed for Bayesian networks. In this paper we propose to upgrade another algorithm, namely ordering-search, since for Bayesian networks this was found to work better than structure-search. We experimentally compare the two upgraded algorithms on two relational domains. We conclude that there is no significant difference between the two algorithms in terms of quality of the learnt models while ordering-search is significantly faster.

Given a binary classification task, a ranker sorts a set of instances from highest to lowest expectation that the instance is positive. We propose a lexicographic ranker, , whose rankings are derived not from scores, but from a simple ranking of attribute values obtained from the training data. When using the odds ratio to rank the attribute values we obtain a restricted version of the naive Bayes ranker. We systematically develop the relationships and differences between classification, ranking, and probability estimation, which leads to a novel connection between the Brier score and ROC curves. Combining with isotonic regression, which derives probability estimates from the ROC convex hull, results in the lexicographic probability estimator . Both and are empirically evaluated on a range of data sets, and shown to be highly effective.

Conventional classification learning allows a classifier to make a one shot decision in order to identify the correct label. However, in many practical applications, the problem is not to give a single estimation, but to make repeated suggestions until the correct target label has been identified. Thus, the learner has to deliver a label ranking, that is, a ranking of all possible alternatives. In this paper, we discuss a loss function, called the position error, which is suitable for evaluating the performance of a label ranking algorithm in this setting. Moreover, we introduce “ranking through iterated choice”, a general strategy for extending any multi-class classifier to this scenario, and propose an efficient implementation of this method by means of pairwise decomposition techniques.

In this paper we study the identification of sparse interaction networks as a machine learning problem. Sparsity mean that we are provided with a small data set and a high number of unknown components of the system, most of which are zero. Under these circumstances, a model needs to be learned that fits the underlying system, capable of generalization. This corresponds to the student-teacher setting in machine learning. In the first part of this paper we introduce a learning algorithm, based on -minimization, to identify interaction networks from poor data and analyze its dynamics with respect to phase transitions. The efficiency of the algorithm is measured by the generalization error, which represents the probability that the student is a good fit to the teacher. In the second part of this paper we show that from a system with a specific system size value the generalization error of other system sizes can be estimated. A comparison with a set of simulation experiments show a very good fit.

Most text categorization methods require text content of documents that is often difficult to obtain. We consider “Collaborative Text Categorization”, where each document is represented by the feedback from a large number of users. Our study focuses on the semi-supervised case in which one key challenge is that a significant number of users have not rated any labeled document. To address this problem, we examine several semi-supervised learning methods and our empirical study shows that collaborative text categorization is more effective than content-based text categorization and the manifold regularization is more effective than other state-of-the-art semi-supervised learning methods.

We introduce a problem called relevant subtask learning, a variant of multi-task learning. The goal is to build a classifier for a task-of-interest having too little data. We also have data for other tasks but only some are relevant, meaning they contain samples classified in the same way as in the task-of-interest. The problem is how to utilize this “background data” to improve the classifier in the task-of-interest. We show how to solve the problem for logistic regression classifiers, and show that the solution works better than a comparable multi-task learning model. The key is to assume that data of all tasks are mixtures of relevant and irrelevant samples, and model the irrelevant part with a sufficiently flexible model such that it does not distort the model of relevant data.

Many applications in information retrieval, natural language processing, data mining, and related fields require a ranking of instances with respect to a specified criteria as opposed to a classification. Furthermore, for many such problems, multiple established ranking models have been well studied and it is desirable to combine their results into a joint ranking, a formalism denoted as . This work presents a novel learning algorithm for rank aggregation (ULARA) which returns a linear combination of the individual ranking functions based on the principle of rewarding ordering agreement between the rankers. In addition to presenting ULARA, we demonstrate its effectiveness on a data fusion task across ad hoc retrieval systems.