Article
Computer Science, Artificial Intelligence
Ching-Hsue Cheng, Yung-Fu Kao, Hsien-Ping Lin
Summary: The study establishes a model for detecting financial statement fraud, addresses missing values and imbalanced classes, proposes useful rules through various methods, utilizes a random forest model, and demonstrates the robustness of ensemble learning in this research.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Robert K. Nowicki, Robert Seliga, Dariusz Zelasko, Yoichi Hayashi
Summary: The paper presents a performance analysis of selected rough set-based classification systems that are hybrid solutions designed to process information with missing values. These systems combine various classification methods with rough set theory. The performance of the systems was analyzed based on classification results obtained from benchmark databases, showing that they exhibit good performance in data classification.
JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Suyun Zhao, Zhigang Dai, Xizhao Wang, Peng Ni, Hengheng Luo, Hong Chen, Cuiping Li
Summary: This study introduces an accelerator for rule induction based on fuzzy rough theory, using consistency degree and key set to speed up the construction of rule classifiers. Experimental results show that the proposed accelerator performs significantly faster than unaccelerated rule-based classifier methods, especially on datasets with numerous instances.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Environmental Studies
Muhammet Deveci, Pablo R. Brito-Parada, Dragan Pamucar, Emmanouil A. Varouchakis
Summary: This study investigates the importance of the seventeen sustainable development goals (SDGs) on sustainable mining. The survey results indicate that SDG8: Decent work and economic growth is perceived as the most important SDG, while SDG14: Life below water is considered the least important.
Article
Computer Science, Theory & Methods
Marko Palangetic, Chris Cornelis, Salvatore Greco, Roman Slowinski
Summary: This paper discusses the importance of granular representations of crisp and fuzzy sets in rule induction algorithms based on rough set theory. It demonstrates that the OWA-based fuzzy rough set model, which has been successfully applied in various machine learning tasks, allows for a granular representation. The practical implications of this result for rule induction from fuzzy rough approximations are highlighted.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Information Systems
Patrick Doherty, Andrzej Szalas
Summary: Reasoning about uncertainty, particularly using probabilistic rough set methods, is a crucial aspect of Knowledge Representation. This paper surveys various approaches in this field and presents a generalization that encompasses all these methods. The authors demonstrate the application of the PROBLOG language in specifying and reasoning about these methods, providing a pragmatic and accessible framework. The paper also introduces new techniques, such as incorporating probabilistic target sets and partially specified base relations. Overall, it offers a comprehensive and practical resource for researchers in the field.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Zhehuang Huang, Jinjin Li
Summary: This paper proposes a new data analysis model using multi-scale coverings for knowledge representation, and discusses optimal scale selection for consistent and inconsistent covering decision tables to obtain acceptable decisions. Experimental results show that the multi-scale covering theory can enhance the generalization ability of the classification model.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Lin Sun, Tengyu Yin, Weiping Ding, Yuhua Qian, Jiucheng Xu
Summary: This article presents a feature selection method based on multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy for multilabel data with missing labels. Experiments verify the effectiveness of the method in recovering missing labels and selecting significant features.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Review
Computer Science, Information Systems
Mustafa Alabadla, Fatimah Sidi, Iskandar Ishak, Hamidah Ibrahim, Lilly Suriani Affendey, Zafienas Che Ani, Marzanah A. Jabar, Umar Ali Bukar, Navin Kumar Devaraj, Ahmad Sobri Muda, Anas Tharek, Noritah Omar, M. Izham Mohd Jaya
Summary: Missing data is a common issue in many domains and can lead to misleading analysis and inaccurate decisions. Machine learning has been widely used in recent years to address this problem more efficiently, improving accuracy, performance, and time consumption. This study provides a comprehensive overview of the latest machine learning imputation methods, aiming to assist researchers in selecting appropriate methods for handling missing values, and offers recommendations for future research.
Article
Computer Science, Information Systems
Zhouming Ma, Jusheng Mi, Yiting Lin, Jinjin Li
Summary: Variable precision rough set (VPRS) has been widely studied as an essential way of knowledge representation and acquisition in uncertainty theory. This paper investigates the corresponding CVPRS model based on a covering-based rough set model, and systematically studies its algebraic structures and properties. An attribute reduction approach is proposed for a covering-based decision information system using the CVPRS model, and the performances of different boundary operators and related indices in these reduction methods are compared. Necessity rules and possibility rules extraction methods corresponding to decision classes are established, and their validity and security are theoretically verified.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Rui Wang, Xiangyu Guo, Shisheng Zhong, Gaolei Peng, Lin Wang
Summary: The article introduces a new decomposition-reorganization method (DRM) to mine rules for machining method chains, which can help technologists design new machining method chains and eliminate the main limitation of existing rough set models. The effectiveness of this method is validated by using three types of shell parts.
JOURNAL OF INTELLIGENT MANUFACTURING
(2022)
Article
Chemistry, Physical
Ha Vinh Lam Nguyen, Kenneth J. Koziol, Tarek Trabelsi, Safa Khemissi, Martin Schwell, Joseph S. Francisco, Isabelle Kleiner
Summary: This study presents the first spectroscopic observation and characterization of the HONO-water complex, providing insights into the impact of water on HONO chemistry and the accuracy of atmospheric measurements. It sheds light on the missing HONO sources and the influence of other molecules in the atmosphere, contributing to a better understanding of atmospheric processes.
JOURNAL OF PHYSICAL CHEMISTRY LETTERS
(2022)
Article
Computer Science, Information Systems
Monalisa Jena, Satchidananda Dehuri
Summary: This work presents an integrated framework combining rule based decision tree and Support Vector Machine for imputation of missing values and prediction of class label, outperforming other methods in terms of performance. Additionally, a new variant of kNN, approximated kNN, is proposed to reduce computational time without compromising classification accuracy.
Article
Computer Science, Artificial Intelligence
Bin Yang
Summary: In this paper, a new type of fuzzy covering-based rough set model over two different universes is proposed using Zadeh's extension principle. The paper focuses on defining fuzzy beta-neighborhood, investigating its properties, defining the new fuzzy covering-based rough set model, and studying its properties. The paper also explores the necessary and sufficient condition for two fuzzy beta-coverings to generate the same fuzzy covering lower approximation or upper approximation. Moreover, the matrix representations of the fuzzy covering lower and upper approximation operators are investigated, and a new approach to a multiple criteria decision making problem is proposed based on the fuzzy covering-based rough set model over two universes.
ARTIFICIAL INTELLIGENCE REVIEW
(2022)
Article
Computer Science, Information Systems
M. A. N. D. Sewwandi, Yuefeng Li, Jinglan Zhang
Summary: This study introduces a novel method, HCluG, to improve granule identification of continuous data by combining hierarchical clustering with neighborhood rough sets, reducing user involvement. Experimental results show that HCluG can reduce the number of features while improving classification performance.
INFORMATION SCIENCES
(2021)
Article
Operations Research & Management Science
Francesca Abastante, Salvatore Corrente, Salvatore Greco, Isabella M. Lami, Beatrice Mecca
Summary: This paper demonstrates the application of multiple criteria decision aiding tools in the analysis of adaptive reuse of a historical building in Turin, Italy. By combining four MCDA methods, the authors propose a decision support approach to identify the preferred alternative.
OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Theory & Methods
Marko Palangetic, Chris Cornelis, Salvatore Greco, Roman Slowinski
Summary: This paper discusses the importance of granular representations of crisp and fuzzy sets in rule induction algorithms based on rough set theory. It demonstrates that the OWA-based fuzzy rough set model, which has been successfully applied in various machine learning tasks, allows for a granular representation. The practical implications of this result for rule induction from fuzzy rough approximations are highlighted.
FUZZY SETS AND SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Milosz Kadzinski, Krzysztof Martyn, Marco Cinelli, Roman Slowinski, Salvatore Corrente, Salvatore Greco
Summary: The paper addresses a problem of multi-decision sorting subject to multiple criteria and presents a new method for dealing with such a problem, including a threshold-based value-driven sorting procedure and constructing interrelated preference models. The practical usefulness of the approach is demonstrated through a case study on risk management related to nanomaterials, highlighting the inferred preference models that can support health and safety managers in reducing associated risks.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Biochemistry & Molecular Biology
Lukasz Palkowski, Maciej Karolak, Jerzy Blaszczynski, Jerzy Krysinski, Roman Slowinski
Summary: This paper presents the results of SAR studies on 140 compounds using DRSA approach, revealing the relationships between compound structure and surface properties with antibacterial activity. Decision rules indicate directions for synthesizing more effective antibacterial compounds. Analysis also showed differences in parameter application for Gram-positive and Gram-negative strains.
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
(2021)
Article
Green & Sustainable Science & Technology
Marco Cinelli, Michael A. Gonzalez, Robert Ford, John McKernan, Salvatore Corrente, Milosz Kadzinski, Roman Slowinski
Summary: This study highlights the importance of selecting appropriate decision support methods in the remediation of contaminated sites through a case study, as well as the necessity for interaction and co-development between decision analysts and stakeholders.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Management
Marco Cinelli, Milosz Kadzinski, Grzegorz Miebs, Michael Gonzalez, Roman Slowinski
Summary: A new methodology for selecting Multiple Criteria Decision Analysis (MCDA) methods is introduced, implemented in the Multiple Criteria Decision Analysis Methods Selection Software (MCDA-MSS). The software provides guidance for analysts in choosing the most suitable MCDA method for a given decision problem, offering a comprehensive evaluation of over 200 MCDA methods based on problem characteristics.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Management
Jose Rui Figueira, Salvatore Greco, Bernard Roy
Summary: The ELECTRE-SCORE method presented in this paper is the first outranking method to assign a score to each alternative. It utilizes outranking relations and reference actions to compare alternatives and assign representative scores within a range, offering a more robust approach compared to traditional methods. By considering imperfect data and avoiding systematic compensatory effects, it provides a reliable evaluation method for decision-making processes.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Marco Cinelli, Peter Burgherr, Milosz Kadzinski, Roman Slowinski
Summary: The current selection of Multiple Criteria Decision Analysis (MCDA) methods in energy systems analysis is problematic, primarily due to the misuse of weighting methods, inappropriate selection of MCDA techniques, and failure to address obvious interactions in preference models. A newly developed methodology can assist decision makers and analysts in choosing the most suitable MCDA method.
DECISION SUPPORT SYSTEMS
(2022)
Article
Computer Science, Information Systems
Marko Palangetic, Chris Cornelis, Salvatore Greco, Roman Slowinski
Summary: Inconsistency refers to the situation where instances that share a certain relationship on condition attributes do not exhibit the same relationship on the decision attribute. Various methods, including rough sets and statistical/machine learning approaches, can be used to handle this inconsistency. The Kotlowski-Slowinski (KS) approach addresses the issue by relabeling objects to remove inconsistencies. In this paper, the KS approach is extended to handle inconsistency determined by a fuzzy preorder relation, leading to a consistent fuzzy relabeling that can be used in binary classification and regression algorithms. The method is supported by statistical foundations, includes optimization procedures, and is illustrated through examples.
INFORMATION SCIENCES
(2023)
Editorial Material
Management
Roman Slowinski
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2023)
Article
Computer Science, Information Systems
Yizhao Zhao, Zaiwu Gong, Guo Wei, Roman Slowinski
Summary: In this paper, a new consensus model for group utility optimization is constructed. The utilities of individual decision-makers are aggregated using 2-additive Choquet integral, and fuzzy measures (weights) consistent with the decision-makers' preferences are learned through linear programming. Furthermore, the coordinator's fairness preference and tolerant behavior are described using Gini coefficient and orness operator, exploring the impact of the coordinator's psychological behaviors on consensus reaching. Finally, a comparative and parametric analysis is performed on a case study regarding the price negotiation of medical insurance drugs to validate the proposed models.
INFORMATION SCIENCES
(2023)
Article
Management
Sally Giuseppe Arcidiacono, Salvatore Corrente, Salvatore Greco
Summary: This paper introduces a method to handle multiple compatible value functions in multi-criteria decision making by building a probability distribution. Stochastic multicriteria acceptability analysis provides statistical information based on the decision maker's preferences. Extensive simulations and sensitivity analysis have been conducted to demonstrate the superiority of the proposed method.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2024)
Article
Management
Salvatore Corrente, Salvatore Greco, Benedetto Matarazzo, Roman Slowinski
Summary: In this paper, we propose an interactive evolutionary multiobjective optimization (IEMO) approach guided by a preference elicitation procedure inspired by artificial intelligence and decision psychology. The approach utilizes decision rules to influence the optimization process and has been proven to converge to the most interesting part of the Pareto front.
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
(2024)
