New PDF release: Advances in Intelligent Data Analysis XIV: 14th

By Elisa Fromont, Tijl De Bie, Matthijs van Leeuwen

This booklet constitutes the refereed convention court cases of the 14th overseas convention on clever facts research, which was once held in October 2015 in Saint Étienne. France. The 29 revised complete papers have been conscientiously reviewed and chosen from sixty five submissions. the normal concentration of the IDA symposium sequence is on end-to-end clever help for info research. The symposium goals to supply a discussion board for uplifting study contributions that will be thought of initial in different best meetings and journals, yet that experience a in all probability dramatic effect. To facilitate this, IDA 2015 will function tracks: a customary "Proceedings" music, in addition to a "Horizon" music for early-stage learn of doubtless ground-breaking nature.

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

Example text

These solvers use a combination of search (assigning a variable to a value) and propagation (per constraint, removing assignments from the domain that would violate that constraint) [17]. Many such generic yet efficient solvers exist. BN Pattern in Constraint Programming (CP). We can encode a BN pattern A = {(X1 = x1 ), . . , (Xm = xm )} in CP by introducing a CP variable Qi for every network variable Xi . g. it is marginalized over, and values 1 . . |D(Xi )| that each represent a possible assignment to the BN variable Xi .

PG (A) = PG (B), we consider those in the set C irrelevant. We call a pattern free if none of its assignments is irrelevant. This definition is similar to the definition of free patterns in data mining [3]. In the presence of a superset constraint, the free constraint should only consider variables that are not required by the superset constraint. Inspired by the related notions of maximality and closedness in frequent itemset mining, we introduce these for BN patterns too. e. e. closed( A, G)).

What are the patterns that have different probabilities according to the original and learned network? It turns out that the pattern {Airbag=False, AntiLock=False, VehicleYear=Older} has the largest difference of probabilities, hence the na¨ıve Bayes model ignores the well-known relation between these three variables (namely older cars are rarely equipped with these safety components). 16 B. Babaki et al. Table 1. The example queries expressed using constraints over pattern A. Q1: probability(A, G, θ), superset(A, {PropertyCost=Million}) Q2: probability(A, G, θ), superset(A, {PropertyCost=Million}), exclude(A, {SeniorTrain, Theft}) Q3: maxprobability(A, G, θ ), f ree(A, G), subset(A, {PropertyCost = Million, DrivingSkill = Substandard, DrivQuality = Poor, LiabilityCost =Thousand}) Q4: exclude(A, {SeniorTrain, GoodStudent}), ev-difference(A, G, {Age=Adolescent}, {Age=Senior}, β) Q5: difference(A, G 1 , G 2 , β) 3 BN Query Framework We now formalize the queries above using constraints over patterns.

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