23,070 research outputs found

    Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection

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    In this paper, we analyze the behavior of the multivariate symmetric uncertainty (MSU) measure through the use of statistical simulation techniques under various mixes of informative and non-informative randomly generated features. Experiments show how the number of attributes, their cardinalities, and the sample size affect the MSU. We discovered a condition that preserves good quality in the MSU under different combinations of these three factors, providing a new useful criterion to help drive the process of dimension reduction

    Stability of the WTP measurements with successive use of choice experiments method and multiple programmes method

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    This paper is part of an investigation to evaluate the benefits of landscape policies. Such policies are, within a specific area (here the Monts d’ArrĂ©e in Brittany), favouring some landscape attributes. We test out a procedure based on a double device. The first one relies on the choice experiments method and focuses on each attribute. Without prior information about the presence of substitution and complementarity effects between attributes, we work on the basis of scenarios built to ensure the independence of attributes. The important question of the impact of an attribute variation on the aesthetic value of another one, when these attributes are jointly perceived, is tackled by use of the multi-programme method. The two surveys were launched after an interval of one year, sampling among the same population. The WTP results obtained from each method are not statistically different.Valuation; choice modelling; multi-attributes choice set; multi-programme method; choice experiments; landscape; Monts d’ArrĂ©e

    Choice modelling in the development of natural resource management strategies in NSW

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    Protecting environmental services generates social benefits. At the same time, private landholders supplying these benefits may face some costs. To provide these services efficiently, policy makers need information about community values for the environment as well as landholders’ costs. This study explores how choice modelling (a non-market valuation technique) is used to estimate comment values. These include use and non-use values for increasing environmental quality in NSW catchments. Non-market valuation techniques for estimating environmental values are reviewed. This is followed by a discussion of methodological aspects of the choice modelling technique and its potential as a regional planning tool for Catchment Management Authorities (CMA’s)Nonmarket valuation, choice modelling, trade-offs, bio-physical modelling, Environmental Economics and Policy, Land Economics/Use,

    The Structure of Rural Landscape in Monetary Evaluation Studies: Main Analytical Approaches in Literature

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    Over recent years considerable research has been devoted to the assessment of the rural landscape value. These studies have concerned both use and non-use value estimation. An important issue in monetary evaluations is about taking (or not) into account the structural complexity of landscape. Three analytical approaches may be recognized on the basis of whether landscape structural attributes are involved (global, mono-attribute and multi-attribute approach). The present work is part of a research aimed to seek out rational instruments for guidance policies on rural landscape. It consists in a survey of the main studies appeared in literature. The specific purpose is to classify these empirical analyses in accordance both to the approaches mentioned above and to the landscape typologies (agricultural or forestry) involved.rural landscape, structural attributes, landscape demand, contingent valuation models, choice experiment, Land Economics/Use, Q26,

    LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances

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    Predicting the completion time of business process instances would be a very helpful aid when managing processes under service level agreement constraints. The ability to know in advance the trend of running process instances would allow business managers to react in time, in order to prevent delays or undesirable situations. However, making such accurate forecasts is not easy: many factors may influence the required time to complete a process instance. In this paper, we propose an approach based on deep Recurrent Neural Networks (specifically LSTMs) that is able to exploit arbitrary information associated to single events, in order to produce an as-accurate-as-possible prediction of the completion time of running instances. Experiments on real-world datasets confirm the quality of our proposal.Comment: Article accepted for publication in 2017 IEEE Symposium on Deep Learning (IEEE DL'17) @ SSC

    Choice Experiments in Enviromental Impact Assessment: The Toro 3 Hydroelectric Project and the Recreo Verde Tourist Center in Costa Rica

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    Choice experiments, a stated preference valuation method, are proposed as a tool to assign monetary values to environmental externalities during the ex-ante stages of environmental impact assessment. This case study looks at the impacts of the Costa Rican Institute of Electricity’s Toro 3 hydroelectric project and its affects on the Recreo Verde tourism center in San Carlos, Costa Rica. Compared to other valuation methods (e.g., travel cost and contingent valuation), choice experiments can create hypothetical but realistic scenarios for consumers and generate restoration alternatives for the affected good. Although they have limitations that must be taken into account in environmental impact assessments, incorporating economic parameters—especially resource constraints and tradeoffs—can substantially enrich the assessment process.stated-preference, economic valuation, choice experiments, hydropower, tourism, Costa Rica
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