Complexity Hierarchies Beyond Elementary

We introduce a hierarchy of fast-growing complexity classes and show its suitability for completeness statements of many non elementary problems. This hierarchy allows the classification of many decision problems with a non-elementary complexity, which occur naturally in logic, combinatorics, formal languages, verification, etc., with complexities ranging from simple towers of exponentials to Ackermannian and beyond.Comment: Version 3 is the published version in TOCT 8(1:3), 2016. I will keep updating the catalogue of problems from Section 6 in future revision

Dominance Measuring Method Performance under Incomplete Information about Weights.

In multi-attribute utility theory, it is often not easy to elicit precise values for the scaling weights representing the relative importance of criteria. A very widespread approach is to gather incomplete information. A recent approach for dealing with such situations is to use information about each alternative?s intensity of dominance, known as dominance measuring methods. Different dominancemeasuring methods have been proposed, and simulation studies have been carried out to compare these methods with each other and with other approaches but only when ordinal information about weights is available. In this paper, we useMonte Carlo simulation techniques to analyse the performance of and adapt such methods to deal with weight intervals, weights fitting independent normal probability distributions orweights represented by fuzzy numbers.Moreover, dominance measuringmethod performance is also compared with a widely used methodology dealing with incomplete information on weights, the stochastic multicriteria acceptability analysis (SMAA). SMAA is based on exploring the weight space to describe the evaluations that would make each alternative the preferred one

Visitors to the city of Évora: Who are they?

Nowadays, driven by multiple factors, tourist demand presents patterned behaviour which is subdivided into several typologies according to destination, product consumed and visitor profile features. In the case of cultural tourism, a good example is that of historic cities, which have their own cultural identity and heritage, and compete to make themselves different from one another through many marketing strategies. This study presents the profile of visitors to the World Heritage City of Évora, including their travel motivations and level of satisfaction with the attributes. Subsequently, the main purpose of this study is to determine the cultural profile of visitors to the World Heritage City of Évora. The data collection technique applied was a visitor survey. The process adopted for the sample definition was a probabilistic sampling, namely the adoption of a stratified sampling plan, by place of residence. Further analysis shows that the most important motivations for visitors in selecting Évora are leisure, heritage/monuments and having a new cultural experience. However they indicate the fact that Évora is considered World Heritage City by UNESCO did influence the decision to visit this destination. Several findings provide the opportunity to establish adequate managerial and marketing strategies to suit the needs of the visitors

QCBA: Postoptimization of Quantitative Attributes in Classifiers based on Association Rules

The need to prediscretize numeric attributes before they can be used in association rule learning is a source of inefficiencies in the resulting classifier. This paper describes several new rule tuning steps aiming to recover information lost in the discretization of numeric (quantitative) attributes, and a new rule pruning strategy, which further reduces the size of the classification models. We demonstrate the effectiveness of the proposed methods on postoptimization of models generated by three state-of-the-art association rule classification algorithms: Classification based on Associations (Liu, 1998), Interpretable Decision Sets (Lakkaraju et al, 2016), and Scalable Bayesian Rule Lists (Yang, 2017). Benchmarks on 22 datasets from the UCI repository show that the postoptimized models are consistently smaller -- typically by about 50% -- and have better classification performance on most datasets