Dominance-based Rough Set Approach, basic ideas and main trends

Dominance-based Rough Approach (DRSA) has been proposed as a machine learning and knowledge discovery methodology to handle Multiple Criteria Decision Aiding (MCDA). Due to its capacity of asking the decision maker (DM) for simple preference information and supplying easily understandable and explainable recommendations, DRSA gained much interest during the years and it is now one of the most appreciated MCDA approaches. In fact, it has been applied also beyond MCDA domain, as a general knowledge discovery and data mining methodology for the analysis of monotonic (and also non-monotonic) data. In this contribution, we recall the basic principles and the main concepts of DRSA, with a general overview of its developments and software. We present also a historical reconstruction of the genesis of the methodology, with a specific focus on the contribution of Roman S{\l}owi\'nski.Comment: This research was partially supported by TAILOR, a project funded by European Union (EU) Horizon 2020 research and innovation programme under GA No 952215. This submission is a preprint of a book chapter accepted by Springer, with very few minor differences of just technical natur

Preference Learning

This report documents the program and the outcomes of Dagstuhl Seminar 14101 “Preference Learning”. Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The motivation for this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies

Integrated decision-making for urban raw water supply in developing countries

2011 Spring.Includes bibliographical references.Rapid urbanization and development are causing severe problems of raw water extraction and related environmental and social impacts in developing countries. This study demonstrated that an integrated approach to decision making could help solve these problems. A case study of raw water management in the region of Jabotabek, Indonesia, which is in and around Jakarta, exhibited social and environmental problems including land-subsidence. The integrated approach was applied in a simulated planning process for raw water development, to include consideration of the economic, environmental and social demands, the hydrological system, and the institutional systems that exist in particular areas. Simulation and optimization techniques (Supply_sim model) were used to determine the planned water allocation for a series of demand clusters for a suite of alternatives and development strategies. A multi-criteria decision analysis (MCDA) based on a decision support system (DSS) was used as an Integrated Decision-Making model to analyze the important and related aspects as one integrated system and to find the best set of decision options. The overall result of the study showed that the integrated approach could improve the decision process to solve the problem. However, its success ultimately depends on the political will of the government to apply the approach. The government needs to improve coordination among the institutions related to raw water supply development and to carry out a transparent decision-making process. Regulations on land-use planning, groundwater abstraction and water pollution control should be applied strictly and aimed to maintain raw water sources. The study also showed that a decision process tool such as the DSS within an integrated framework of decision making could help decision makers to reach consensus and gain stakeholder participation, accountability and commitment to the decision being made. In dealing with complex raw water problems in large cities, the study also showed that planning systems could help decision makers to think systematically to improve the decision results

A decade of application of the Choquet and Sugeno integrals in multi-criteria decision aid

The main advances regarding the use of the Choquet and Sugeno integrals in multi-criteria decision aid over the last decade are reviewed. They concern mainly a bipolar extension of both the Choquet integral and the Sugeno integral, interesting particular submodels, new learning techniques, a better interpretation of the models and a better use of the Choquet integral in multi-criteria decision aid. Parallel to these theoretical works, the Choquet integral has been applied to many new fields, and several softwares and libraries dedicated to this model have been developed.Choquet integral, Sugeno integral, capacity, bipolarity, preferences

Multiple Criteria Ranking and Choice with All Compatible Minimal Cover Sets of Decision Rules

We introduce a new multiple criteria ranking/choice method that applies Dominance-based Rough Set Approach (DRSA) and represents the Decision Maker's (DM's) preferences with decision rules. The DM provides a set of pairwise comparisons indicating whether an outranking (weak preference) relation should hold for some pairs of reference alternatives. This preference information is structured using the lower and upper approximations of outranking (S) and non-outranking (S c ) relations. Then, all minimal-cover (MC) sets of decision rules being compatible with this preference information are induced. Each of these sets is supported by some positive examples (pairs of reference alternatives from the lower approximation of a preference relation) and it does not cover any negative example (pair of alternatives from the upper approximation of an opposite preference relation). The recommendations obtained by all MC sets of rules are analyzed to describe pairwise outranking and non-outranking relations, using probabilistic indices (estimates of probabilities that one alternative outranks or does not outrank the other). Furthermore, given the preference relations obtained in result of application of each MC set of rules on a considered set of alternatives, we exploit them using some scoring procedures. From this, we derive the distribution of ranks attained by the alternatives. We also extend the basic approach in several ways. The practical usefulness of the method is demonstrated on a problem of ranking Polish cities according to their innovativeness

Uncertain Multi-Criteria Optimization Problems

Most real-world search and optimization problems naturally involve multiple criteria as objectives. Generally, symmetry, asymmetry, and anti-symmetry are basic characteristics of binary relationships used when modeling optimization problems. Moreover, the notion of symmetry has appeared in many articles about uncertainty theories that are employed in multi-criteria problems. Different solutions may produce trade-offs (conflicting scenarios) among different objectives. A better solution with respect to one objective may compromise other objectives. There are various factors that need to be considered to address the problems in multidisciplinary research, which is critical for the overall sustainability of human development and activity. In this regard, in recent decades, decision-making theory has been the subject of intense research activities due to its wide applications in different areas. The decision-making theory approach has become an important means to provide real-time solutions to uncertainty problems. Theories such as probability theory, fuzzy set theory, type-2 fuzzy set theory, rough set, and uncertainty theory, available in the existing literature, deal with such uncertainties. Nevertheless, the uncertain multi-criteria characteristics in such problems have not yet been explored in depth, and there is much left to be achieved in this direction. Hence, different mathematical models of real-life multi-criteria optimization problems can be developed in various uncertain frameworks with special emphasis on optimization problems

Guidance on the integrated assessment of complex health technologies: the INTEGRATE-HTA model

Challenges in assessments of health technologies In recent years there have been major advances in the development of health technology assessment (HTA). However, HTA still has certain limitations when assessing technologies which are complex, i.e. consist of several interacting components, target different groups or organizational levels, have multiple and variable outcomes, and/or permit a certain degree of flexibility or tailoring (Craig et al., 2008), fi are context-dependent - current HTA usually focusses on the technology, not on the system within which it is used, fi perform differently depending on the way they are implemented, fi have different effects on different individuals. Furthermore, HTA usually assesses and appraises aspects side-by-side, while decision-making needs an integrated perspective on the value of a technology. In the EU-funded INTEGRATE-HTA project, we developed concepts and methods to deal with these challenges, which are described in six guidances. Because of the interactions, an integrated assessment needs to start from the beginning of the assessment. This guidance provides a systematic five-step-process for an integrated assessment of complex technologies (the INTEGRATE-HTA Model). Purpose and scope of the guidance The aim of the INTEGRATE-HTA project is to provide concepts and methods that enable a patient-centred, comprehensive, and integrated assessment of complex health technologies. The purpose of this guidance is to structure the overall HTA-process. The INTEGRATE-HTA Model outlines an integrated scoping process, a coordinated application of assessment methods for different aspects and an integrated and structured decision-making process. It is intended for HTA agencies, HTA researchers and those engaged in the evaluation of complex health technologies. As it links the assessment to the decision-making process, it also addresses HTA commissioners and other stakeholders using or planning HTAs. While all technologies are arguably complex, some are more complex than others. Applying this guidance might lead to a more thorough and therefore more time-consuming process. Depending on the degree of complexity, one might choose to follow the whole process as described in this guidance, or only focus on certain steps. The guidance provides an operational definition to assess the complexity of technologies which can be used to identify specific aspects that will need more attention than others. What the guidance does not provide is a post-hoc solution for assessments that have already been completed. | 6 Development of the guidance The INTEGRATE-HTA Model presented in this guidance was developed based on a systematic literature search on approaches for integration, on the experiences of traditional HTAs, as well as on the other methodological guidances developed in the INTEGRATE-HTA project. It was tested in a case study on palliative care and iteratively revised during the practical application. The guidance was again revised after internal and external peer-review. Application of this guidance For a comprehensive integrated assessment of a complex technology, we developed a five-step process, the INTEGRATE-HTA model. In Step 1, the HTA objective and the technology are defined with the support from a panel of stakeholders. An initial logic model is developed in Step 2. The initial logic model provides a structured overview of the technology, the context, implementation issues, and relevant patient groups. It then frames the assessment of the effectiveness, as well as economic, ethical, legal, and socio-cultural aspects in Step 3. In Step 4, a graphical overview of the assessment results, structured by the logic model, is provided. Step 5 is a structured decision-making process informed by the HTA (and is thus not formally part of the HTA, but follows it). fi Step 1: In step 1, the technology under assessment and the objective of the HTA are defined. Especially for complex technologies, such as palliative care, the definition of the technology alone is a challenge that must not be underestimated. It is recommended to do this based on a tentative literature review and with the support of stakeholder advisory panels (SAPs) which should comprise clinical experts, academics, patients, possibly their relatives and/or other caretakers, and the public. The setting of an objective considering all relevant aspects of complexity and structured by assessment criteria is important. The assessment criteria will usually reflect values of the stakeholders as well as the input from the theoretical, methodological and empirical literature. fi Step 2: In step 2, an initial logic model is developed (see Guidance on the use of logic models in health technology assessments of complex interventions). The model provides a structured overview on participants, interventions, comparators, and outcomes. Parallel to this, groups of patients that are distinguished by different preferences and treatment moderators (see Guidance for the assessment of treatment moderation and patients’ preferences) are identified. Specific context and implementation issues are also identified as part of the initial logic model (see Guidance for the Assessment of Context and Implementation in Health Technology Assessments (HTA) and Systematic Reviews of Complex Interventions). The product of this step is the logic model as a graphical representation of all aspects and their interactions that are relevant for the assessment of the complex technology. fi Step 3: In step 3, the logic model serves as a conceptual framework that guides the evidence assessment. Depending on the specific aspect (e.g. effectiveness, economic, ethical, socio-cultural, or legal aspects) different methods are available for the assessment (see Guidance for assessing effectiveness, economic aspects, ethical aspects, socio-cultural aspects and legal aspects in complex technologies). The outputs of step 3 are evidence reports and standardized evidence summaries for each assessment aspect (e.g. report on economics, report on ethical aspects, etc.). fi Step 4: In step 4, the assessment results of step 3 are structured using the logic model developed in step 2. Whereas the initial logic model in step 2 specifies what evidence is relevant, the extended logic model to assist decision-making in step 4 visualizes the assessment results as well as the interaction with respect to the HTA objectives. It also allows for the consideration of different scenarios depending on the variation in context, implementation and patient characteristics. 7 | fi Step 5: Step 5 involves a structured decision-making process and is not an integral part of the HTA in the narrow sense. Decision-making can be supported by applying quantitative e.g. MCDA- (Multi-criteria decision analysis) or qualitative decision support tools. Flexibility in the application of these tools by the decision committee is crucial, taking different decision settings and evidence needs into consideration. Conclusions In current HTA, different aspects are usually assessed and presented independent of each other. Context, implementation issues and patient characteristics are rarely considered. The INTEGRATE-HTA Model enables a coordinated assessment of all these aspects and addresses their interdependencies. The perspective of stakeholders such as patients and professionals with their values and preferences is integrated in the INTEGRATE-HTA Model to obtain HTA results that are meaningful for all relevant stakeholders. Finally, health policy makers obtain an integrated perspective of the assessment results to achieve fair and legitimate conclusions at the end of the HTA process. The application of the model will usually require more time and resources than traditional HTA. An initial assessment of the degree and the character of complexity of a technology might be helpful to decide whether or not the whole process or only specific elements will be applied

Electre Methods: Main Features and Recent Developments

We present main characteristics of Electre family methods, designed for multiple criteria decision aiding. These methods use as a preference model an outranking relation in the set of actions - it is constructed in result of concordance and non-discordance tests involving a specific input preference information. After a brief description of the constructivist conception in which the Electre methods are inserted, we present the main features of these methods. We discuss such characteristic features as: the possibility of taking into account positive and negative reasons in the modeling of preferences, without any need for recoding the data; using of thresholds for taking into account the imperfect knowledge of data; the absence of systematic compensation between "gains" and "losses". The main weaknesses are also presented. Then, some aspects related to new developments are outlined. These are related to some new methodological developments, new procedures, axiomatic analysis, software tools, and several other aspects. The paper ends with conclusions