Advances in Intelligent Data Analysis XIV: 14th by Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen

By Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen

This publication constitutes the refereed convention lawsuits of the 14th foreign convention on clever facts research, which used to be held in October 2015 in Saint Étienne. France. The 29 revised complete papers have been rigorously reviewed and chosen from sixty five submissions. the conventional concentration of the IDA symposium sequence is on end-to-end clever aid for info research. The symposium goals to supply a discussion board for uplifting learn contributions that will be thought of initial in different best meetings and journals, yet that experience a in all likelihood dramatic effect. To facilitate this, IDA 2015 will function tracks: a standard "Proceedings" song, in addition to a "Horizon" music for early-stage study of probably ground-breaking nature.

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Extra resources for Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA 2015, Saint Etienne, France, October 22–24, 2015, Proceedings

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A variable neighborhood search algorithm for the optimization of a dial-a-ride problem in a large city. Expert Syst. Appl. 40(14), 5516–5531 (2013) 11. : Metaheuristics for the dynamic stochastic dial-a-ride problem with expected return transports. Comput. Oper. Res. 38(12), 1719–1730 (2011) 12. : Optimisation for the ride-sharing problem: a complexity-based approach. In: ECAI 2014–21st European Conference on Artificial Intelligence, 18–22 August 2014, Czech Republic - Including Prestigious Applications of Intelligent Systems (PAIS 2014), pp.

In this paper, we exploit the similarities between these two fields and introduce constraint-based queries for Bayesian networks. The contribution of this paper is three-fold. First, inspired by constraintbased mining, we introduce an expressive set of exploratory queries for Bayesian networks. Secondly, we identify how these queries can be expressed as constraints over the variables and joint distribution of the Bayesian network. Finally, we show how these constraints can be expressed as a generic constraint program, combining ideas from constraint programming, itemset mining and knowledge compilation, in particular CP4IM [9] and arithmetic circuits (AC) [5].

Table 3 reports BN and AC size, θ threshold and runtimes. AC compilation time is small. Observe that in Table 3 the two larger networks have smaller AC’s, because of their other structural properties (see [5] for more details). While bigger ACs require more runtime, the number of solutions has a major impact on runtime too. This can be controlled up to some extend by adding extra constraints. Table 3. Time: solving time. edu/ace/. org. com/bnrepository/. 66 22 B. Babaki et al. Comparison with Sampling.

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