This paper represents a technique, applying user action patterns in order to distinguish between users and identify them. In this method, users' actions sequences are mapped to numerical sequences and each user...
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This work studies the problem of premature convergence due to the lack of diversity in Estimation of Distributions Algorithms. This problem is quite important for these kind of algorithms since, even when using very c...
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This work studies the problem of premature convergence due to the lack of diversity in Estimation of Distributions Algorithms. This problem is quite important for these kind of algorithms since, even when using very complex probabilistic models, they can not solve certain optimization problems such as some deceptive, hierarchical or multimodal ones. There are several works in literature which propose different techniques to deal with premature convergence. In most cases, they arise as an adaptation of the techniques used with genetic algorithms, and use randomness to generate individuals. In our work, we study a new scheme which tries to preserve the population diversity. Instead of generating individuals randomly, it uses the information contained in the probability distribution learned from the population. In particular, a new probability distribution is obtained as a variation of the learned one so as to generate individuals with less probability to appear on the evolutionary process. This proposal has been validated experimentally with success with a set of different test functions.
Structural learning of Bayesian networks (BNs) is an NP-hard problem generally addressed by means of heuristic search algorithms. Although these techniques do not guarantee an optimal result, they allow obtaining good...
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Structural learning of Bayesian networks (BNs) is an NP-hard problem generally addressed by means of heuristic search algorithms. Although these techniques do not guarantee an optimal result, they allow obtaining good solutions with a relatively low computational effort. Many proposals are based on searching the space of directed acyclic graphs. However, there are alternatives consisting of exploring the space of equivalence classes of BNs, which yields more complex and difficult to implement algorithms, or the space of the orderings among variables. In practice, ordering-based methods allow reaching good results, but, they are costly in terms of computation. In this paper, we prove the correctness of the method used to evaluate each permutation when exploring the space of orderings, and we propose two simple and efficient learning algorithms based on this approach. The first one is a Hill climbing method which uses an improved neighbourhood definition, whereas the second algorithm is its natural extension based on the well-known variable neighbourhood search metaheuristic. The algorithms have been tested over a set of different domains in order to study their behaviour in practice.
A datamining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of datamining functions, i...
A datamining system, DBMiner, has been developed for interactive mining of multiple-level knowledge in large relational databases and data warehouses. The system implements a wide spectrum of datamining functions, including characterization, comparison, association, classification, prediction, and clustering. By incorporating several interesting datamining techniques, including OLAP and attribute-oriented induction, statistical analysis, progressive deepening for mining multiple-level knowledge, and meta-rule guided mining, the system provides a user-friendly, interactive datamining environment with good performance.
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