Catálogo de publicaciones - libros

In this paper we investigate the use of SAT technology for solving constraint problems. In particular, we solve many instances of several common benchmark problems for CP with different SAT solvers, by exploiting the declarative modelling language , and , an application that allows us to compile specifications into SAT instances. Furthermore, we start investigating whether some reformulation techniques already used in CP are effective when using SAT as solving engine. We present encouraging experimental results in this direction, showing that this approach can be appealing.

This paper presents Dependency Tree Semantics (DTS), an underspecified logic for representing quantifier scope ambiguities. DTS features a direct interface with a Dependency grammar, an easy management of partial disambiguations and the ability to represent branching quantifier readings. This paper focuses on the syntax of DTS, while does not take into account the model-theoretic interpretation of its well-formed structures.

Many problems encountered in practice involve the prediction of a continuous attribute associated with an example. This problem, known as regression, requires that samples of past experience with known continuous answers are examined and generalized in a regression model to be used in predicting future examples. Regression algorithms deeply investigated in statistics, machine learning and data mining usually lack measures to give an indication of how “good” the predictions are. Tolerance regions, i.e., a range of possible predictive values, can provide a measure of reliability for every bare prediction. In this paper, we focus on tree-based prediction models, i.e., model trees, and resort to the inductive inference to output tolerance regions in addition to bare prediction. In particular, we consider model trees mined by SMOTI (Stepwise Model Tree Induction) that is a system for data-driven stepwise construction of model trees with regression and splitting nodes and we extend the definition of trees to build tolerance regions to be associated with each leaf. Experiments evaluate validity and quality of output tolerance regions.

This work presents a method founded on instance-based learning algorithms for inductive (memory-based) reasoning on ABoxes. The method, which exploits a semantic dissimilarity measure between concepts and instances, can be employed both to infer class membership of instances and to predict hidden assertions that are not logically entailed from the knowledge base and need to be successively validated by humans (e.g. a knowledge engineer or a domain expert). In the experimentation, we show that the method can effectively help populating an ontology with likely assertions that could not be logically derived.

In this study we explore the feasibility of applying Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to the prediction of incipient power transformer faults. A clonal selection algorithm (CSA) is introduced for the first time in the literature to select optimal input features and RBF kernel parameters. CSA is shown to be capable of improving the speed and accuracy of classification systems by removing redundant and potentially confusing input features, and of optimizing the kernel parameters simultaneously. Simulation results on practice data demonstrate the effectiveness and high efficiency of the proposed approach.

Separate-and-conquer classifiers strongly depend on the criteria used to choose which rules will be included in the classification model. When association rules are employed to build such classifiers (as in ART [3]), rule evaluation can be performed attending to different criteria (other than the traditional confidence measure used in association rule mining). In this paper, we analyze the desirable properties of such alternative criteria and their effect in building rule-based classifiers using a separate-and-conquer strategy.

Using Bayesian networks as the representation language for student modeling has become a common practice. Many computer-assisted learning systems rely exclusively on human experts to provide information for constructing the network structures, however. We explore the possibility of applying mutual information-based heuristics and support vector machines to learn how students learn composite concepts, based on students’ item responses to test items. The problem is challenging because it is well known that students’ performances in taking tests do not reflect their competences faithfully. Experimental results indicate that the difficulty of identifying the true learning patterns varies with the degree of uncertainty in the relationship between students’ performances in tests and their abilities in concepts. When the degree of uncertainty is moderate, it is possible to infer the unobservable learning patterns from students’ external performances with computational techniques.

Discretization of the value range of a numerical feature is a common task in data mining and machine learning. Optimal multivariate discretization is in general computationally intractable. We have proposed approximation algorithms with performance guarantees for training error minimization by axis-parallel hyperplanes. This work studies their efficiency and practicability. We give efficient implementations to both greedy set covering and linear programming approximation of optimal multivariate discretization. We also contrast the algorithms empirically to an efficient heuristic discretization method.

This paper deals with an optimization of Random Forests which aims at: adapting the concept of forest for learning imbalanced data as well as taking into account user’s wishes as far as recall and precision rates are concerned. We propose to adapt Random Forest on two levels. First of all, during the forest creation thanks to the use of asymmetric entropy measure associated to specific leaf class assignation rules. Then, during the voting step, by using an alternative strategy to the classical majority voting strategy. The automation of this second step requires a specific methodology for results quality assessment. This methodology allows the user to define his wishes concerning (1) recall and precision rates for each class of the concept to learn, and, (2) the importance he wants to confer to each one of those classes. Finally, results of experimental evaluations are presented.

This paper describes the study on example selection in regression problems using -SVM (Support Vector Machine) linear as prediction algorithm. The motivation case is a study done on real data for a problem of bus trip time prediction. In this study we use three different training sets: all the examples, examples from past days similar to the day where prediction is needed, and examples selected by a CART regression tree. Then, we verify if the CART based example selection approach is appropriate on different regression data sets. The experimental results obtained are promising.