{"title":"Bipolar decomposition integrals","authors":"Jabbar Abbas , Radko Mesiar , Radomír Halaš","doi":"10.1016/j.ijar.2025.109439","DOIUrl":null,"url":null,"abstract":"<div><div>The idea of decomposition integral, inspired by the concept of Lebesgue integral, is a common framework for unifying many nonlinear integrals, such as the Choquet, the Shilkret, the PAN, and the concave integrals. This framework concerns aggregation on a unipolar scale, and depends on the distinguished decomposition system under some constraints on the sets being considered for each related integral. The aim of this paper is to provide a general framework to deal with integrals concerning aggregation on unipolar and bipolar scales. To achieve this aim, we propose in this paper an extension of the idea of decomposition integral of the integrated function to be suitable for bipolar scales depending on the distinguished bipolar decomposition system under some constraints on the bipolar collections being considered for each related bipolar fuzzy integral. Then, we introduce some properties of bipolar decomposition integrals, including those establishing that our approach covers the Cumulative Prospect Theory (CPT) model and the integrals with respect to bipolar capacities. Finally, we conclude with certain directions on some additional findings related to the research.</div></div>","PeriodicalId":13842,"journal":{"name":"International Journal of Approximate Reasoning","volume":"183 ","pages":"Article 109439"},"PeriodicalIF":3.2000,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Approximate Reasoning","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0888613X25000805","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
The idea of decomposition integral, inspired by the concept of Lebesgue integral, is a common framework for unifying many nonlinear integrals, such as the Choquet, the Shilkret, the PAN, and the concave integrals. This framework concerns aggregation on a unipolar scale, and depends on the distinguished decomposition system under some constraints on the sets being considered for each related integral. The aim of this paper is to provide a general framework to deal with integrals concerning aggregation on unipolar and bipolar scales. To achieve this aim, we propose in this paper an extension of the idea of decomposition integral of the integrated function to be suitable for bipolar scales depending on the distinguished bipolar decomposition system under some constraints on the bipolar collections being considered for each related bipolar fuzzy integral. Then, we introduce some properties of bipolar decomposition integrals, including those establishing that our approach covers the Cumulative Prospect Theory (CPT) model and the integrals with respect to bipolar capacities. Finally, we conclude with certain directions on some additional findings related to the research.
期刊介绍:
The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest.
Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning.
Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.