A5070170207- Rough Sets and Fuzzy Logic
- Multi-Criteria Decision Making
- Data Mining Algorithms and Applications
- Statistical and Computational Modeling
- Fuzzy Systems and Optimization
- Data Management and Algorithms
- Optimization and Mathematical Programming
- Bayesian Modeling and Causal Inference
- Advanced Multi-Objective Optimization Algorithms
- Scheduling and Optimization Algorithms
- Fuzzy Logic and Control Systems
- Imbalanced Data Classification Techniques
- Resource-Constrained Project Scheduling
- Advanced Algebra and Logic
- AI-based Problem Solving and Planning
- Semantic Web and Ontologies
- Water resources management and optimization
- Evolutionary Algorithms and Applications
- Image Processing and 3D Reconstruction
- Metaheuristic Optimization Algorithms Research
- Neural Networks and Applications
- Cognitive Science and Mapping
- Machine Learning and Data Classification
- Advanced Statistical Methods and Models
- Financial Distress and Bankruptcy Prediction
Poznań University of Technology
2015-2024
Systems Research Institute
2015-2024
Polish Academy of Sciences
2015-2024
Virginia Tech
2024
ETH Zurich
2018
Swiss Re (Switzerland)
2018
Paul Scherrer Institute
2018
University of Catania
2011-2018
University of Portsmouth
2018
Libera Università Maria SS. Assunta
2018
Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems be of fundamental importance artificial intelligence (AI) cognitive sciences, especially areas machine learning, knowledge acquisition, decision analysis, discovery from databases, expert systems, support inductive reasoning, pattern recognition.
This paper proposes new definitions of lower and upper approximations, which are basic concepts the rough set theory. These follow naturally from concept ambiguity introduced in this paper. The compared to classical shown be more general, sense that they only ones can used for any type indiscernibility or similarity relation.
In this article we are considering a multicriteria classification that differs from usual problems since it takes into account preference orders in the description of objects by condition and decision attributes. To deal with propose to use dominance-based rough set approach (DRSA). This is different classic (CRSA) because domains attributes classes. Given partitioned pre-defined preference-ordered classes, new able approximate partition means dominance relations (instead indiscernibility...
ABSTRACT We present main characteristics of ELECTRE (ELimination Et Choix Traduisant la REalité ‐ ELimination and Choice Expressing the REality) family methods, designed for multiple criteria decision aiding. These methods use as a preference model an outranking relation on set actions—it is constructed in result concordance nondiscordance tests involving specific input information. After brief description constructivist conception which are inserted, we features these methods. discuss such...
Decision making is a complex task that involves multitude of perspectives, constraints, and variables. Multiple Criteria Analysis (MCDA) process has been used for several decades to support decision making. It includes series steps systematically help Maker(s) (DM(s)) stakeholders in structuring problem, identifying their preferences, building recommendation consistent with those preferences. Over the last decades, many studies have demonstrated conduct MCDA how select an method. Until now,...
We formulate some questions that may help an analyst to choose a multicriteria decision aiding method well adapted the context. These take into account several aspects of process and cooperation between maker. present these in hierarchical order, from most general crucial, through other pertinent concerning aggregation, secondary ones. The initial question is what type results expected bring. next concern requirements on preference scales, acquisition information, handling imperfect...
We present a new methodology to lead the selection of Multiple Criteria Decision Analysis (MCDA) methods. It is implemented in Methods Selection Software (MCDA-MSS), decision support system that helps analysts answer recurring question science: "Which most suitable method (or subset MCDA methods) should be used for given Decision-Making Problem (DMP)?". The MCDA-MSS provides guidance decision-making processes and choose among an extensive collection (>200) These are assessed according...
The ability to understand and explain the outcomes of data analysis methods, with regard aiding decision-making, has become a critical requirement for many applications. For example, in operational research domains, analytics have long been promoted as way enhance decision-making. This study proposes comprehensive, normative framework define explainable artificial intelligence (XAI) (XAIOR) reconciliation three subdimensions that constitute its requirements: performance, attributable,...
A bstract We present a new approach to evaluation of bankruptcy risk firms based on the rough set theory. The concept appeared be an effective tool for analysis information systems representing knowledge gained by experience. financial system describes objects (firms) multi‐valued attributes (financial ratios and qualitative variables), called condition attributes. are classified into groups subject expert's opinion, decision attribute. natural problem consists then in discovering...