다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Data Mining Practical Machine Learning Tools and Techniques

Statistical Analysis with Missing Data

A freely available dataset produced from three million Landsat satellite images reveals substantial changes in the distribution of global surface water over the past 32 years and their causes, from climate change to human actions. The distribution of surface water has been mapped globally, and local-to-regional studies have tracked changes over time. But to date, there has been no global and methodologically consistent quantification of changes in surface water over time. Jean-Francois Pekel and colleagues have analysed more than three million Landsat images to quantify month-to-month changes in surface water at a resolution of 30 metres and over a 32-year period. They find that surface waters have declined by almost 90,000 square kilometres—largely in the Middle East and Central Asia—but that surface waters equivalent to about twice that area have been created elsewhere. Drought, reservoir creation and water extraction appear to have driven most of the changes in surface water over the past decades. The location and persistence of surface water (inland and coastal) is both affected by climate and human activity1 and affects climate2,3, biological diversity4 and human wellbeing5,6. Global data sets documenting surface water location and seasonality have been produced from inventories and national descriptions7, statistical extrapolation of regional data8 and satellite imagery9,10,11,12, but measuring long-term changes at high resolution remains a challenge. Here, using three million Landsat satellite images13, we quantify changes in global surface water over the past 32 years at 30-metre resolution. We record the months and years when water was present, where occurrence changed and what form changes took in terms of seasonality and persistence. Between 1984 and 2015 permanent surface water has disappeared from an area of almost 90,000 square kilometres, roughly equivalent to that of Lake Superior, though new permanent bodies of surface water covering 184,000 square kilometres have formed elsewhere. All continental regions show a net increase in permanent water, except Oceania, which has a fractional (one per cent) net loss. Much of the increase is from reservoir filling, although climate change14 is also implicated. Loss is more geographically concentrated than gain. Over 70 per cent of global net permanent water loss occurred in the Middle East and Central Asia, linked to drought and human actions including river diversion or damming and unregulated withdrawal15,16. Losses in Australia17 and the USA18 linked to long-term droughts are also evident. This globally consistent, validated data set shows that impacts of climate change and climate oscillations on surface water occurrence can be measured and that evidence can be gathered to show how surface water is altered by human activities. We anticipate that this freely available data will improve the modelling of surface forcing, provide evidence of state and change in wetland ecotones (the transition areas between biomes), and inform water-management decision-making.

High-resolution mapping of global surface water and its long-term changes

Dealing with uncertainty is always a challenging problem, and different tools have been proposed to deal with it. Recently, a new model that is based on hesitant fuzzy sets has been presented to manage situations in which experts hesitate between several values to assess an indicator, alternative, variable, etc. Hesitant fuzzy sets suit the modeling of quantitative settings; however, similar situations may occur in qualitative settings so that experts think of several possible linguistic values or richer expressions than a single term for an indicator, alternative, variable, etc. In this paper, the concept of a hesitant fuzzy linguistic term set is introduced to provide a linguistic and computational basis to increase the richness of linguistic elicitation based on the fuzzy linguistic approach and the use of context-free grammars by using comparative terms. Then, a multicriteria linguistic decision-making model is presented in which experts provide their assessments by eliciting linguistic expressions. This decision model manages such linguistic expressions by means of its representation using hesitant fuzzy linguistic term sets.

/pdf/hesitant-fuzzy-linguistic-term-sets-for-decision-making-4y2hc7e6bo.pdf

Hesitant Fuzzy Linguistic Term Sets for Decision Making

In this paper, we discuss the functionality and interfaces of five MCDM software packages. We recognize that no single package is appropriate for all decision contexts and processes. Thus our emphasis is not so much to compare the functionality of the packages per se, but to consider their fit with different decision making processes. In doing so, we hope to provide potential users with guidance on selecting a package that is more compatible with their needs. Moreover, we reflect on the further functionality which we would believe should be developed and included in MCDM packages. Copyright � 2006 John Wiley & Sons, Ltd.

Comparison study of multi-attribute decision analytic software

Attribute weight assignment plays an important role in multiple attribute decision analysis (MADA). When the performances of alternatives on each attribute are expressed by distributions instead of single values, how to use the differences among the performances to obtain attribute weights is an interesting but difficult issue. To address this issue, in this paper, we propose a method for obtaining attribute weights from discriminating power in belief distributions. With the consideration of the differences among the utilities of all assessment grades used to profile belief distributions, the dissimilarity based discriminating power, the standard deviation based discriminating power and the Gini's mean difference based discriminating power of the performances of all alternatives on each attribute are constructed to determine three sets of respective weights of attributes. They are convexly combined using three coefficients to generate integrated weights of attributes. To relieve the burden on a decision maker to provide precise values for the three coefficients, they are allowed to change between 0 and 1, as long as their sum is equal to 1. Under such constraints on the three coefficients, an optimization model is constructed to determine the minimum and maximum expected utilities of each alternative. From the expected utilities, all alternatives are then compared using Hurwicz rule with the provided optimism degree interval to generate solutions to MADA problems. The transitivity of the comparison outcomes among three alternatives under a given optimism degree interval is theoretically analyzed to guarantee the rationality of the outcomes. A focal form selection problem is investigated to demonstrate the applicability and validity of the proposed method.

/pdf/determining-attribute-weights-for-multiple-attribute-4i76xz7a00.pdf

Determining attribute weights for multiple attribute decision analysis with discriminating power in belief distributions

A method is presented for selecting a preferred ship from a group of candidates as a reference ship for a new design. The method is based on a recently developed approach for multiple-criteria decision analysis under uncertainty, the evidential reasoning approach. Using this method, both quantitative and qualitative attributes of a complicated nature can be considered in the selection process. The method consists of three phases: identifying suitable candidate ships, evaluating them in terms of both conventional techno-economical and qualitative attributes, and aggregating all the attributes using the evidential reasoning approach. This three-phase procedure is illustrated by means of an oil tanker selection example. The results of this study show that the evidential reasoning approach can support multiple-criteria ship selection processes when both qualitative and quantitative information with or without uncertainties have to be taken into account. The outcomes generated by the method include the ranking of the candidate ships and indications of their strengths and weaknesses in the format of performance distributions over different assessment grades. Such information is vital in helping decision makers to make an informed selection and be aware of any risk implication associated with the selection.

/pdf/ship-selection-using-a-multiple-criteria-synthesis-approach-3if3e0q8i1.pdf

Ship selection using a multiple-criteria synthesis approach

In this paper the relationship between Bayes' rule and the Evidential Reasoning (ER) rule is explored. The ER rule has been uncovered recently for inference with multiple pieces of uncertain evidence profiled as a belief distribution and takes Dempster's rule in the evidence theory as a special case. After a brief introduction to the ER rule the conditions under which Bayes' rule becomes a special case of the ER rule are established. The main findings include that the normalisation of likelihoods in Bayesian paradigm results in the degrees of belief in the ER paradigm. This leads to ER-based probabilistic (likelihood) inference with evidence profiled in the same format of belief distribution. Numerical examples are examined to demonstrate the findings and their potential applications in probabilistic inference. It is also demonstrated that the findings enable the generalisation of Bayesian inference to evidential reasoning with inaccurate probability information with weight and reliability.

/pdf/a-study-on-generalising-bayesian-inference-to-evidential-15dm73wh1v.pdf

A Study on Generalising Bayesian Inference to Evidential Reasoning

Intelligent decision system (IDS) is a window-based software package that has been developed on the basis of the evidential reasoning (ER) approach, a recent development in handling hybrid multiple criteria decision analysis (MCDA) problems with uncertainties. In this paper, the evidential reasoning approach will be briefly described first, and its major differences from and the relationships with conventional MCDA methods will also be discussed. Then the main features, advantages and benefits of IDS will be demonstrated and explained using two application examples: supplier pre-qualification assessment and customer satisfaction survey analysis, which have been investigated as part of the research projects led by the authors and funded by the UK government and the EC. It is concluded in the paper that the ER approach can be used not only to deal with problems that traditional methods can solve, but also to model and analyse more complicated decision problems that traditional methods are incapable of handling. Keywords— multicriteria decision support systems, knowledge management, intelligent decision system, the evidential reason-

/pdf/intelligent-decision-system-based-on-the-evidential-33hgf7nv6r.pdf

Dong-Ling Xu

Papers

Comparison study of multi-attribute decision analytic software

Determining attribute weights for multiple attribute decision analysis with discriminating power in belief distributions

Ship selection using a multiple-criteria synthesis approach

A Study on Generalising Bayesian Inference to Evidential Reasoning

Intelligent decision system based on the evidential reasoning approach and its applications