Exploring Intuitionistic Fuzzy-Valued Neutrosophic Multiset Technique for High-Dimensional Financial Data Classification in Complex Systems
Hafis Hajiyev1,*, Emil Hajiyev2, Zarnigor Ilkhamova3, Elena Klochko4, E. Laxmi Lydia5
1Department of Finance and Audit, Azerbaijan State University of Economics (UNEC), Baku, AZ1001, Azerbaijan
2Department of Business Management, Azerbaijan State University of Economics (UNEC), Baku, AZ1001, Azerbaijan
3Department of Management and Marketing, Urgench State University, Urgench, 220100, Uzbekistan
4Department of Management, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, 350044, Russia
5Department of Information Technology, VR Siddhartha Engineering College (A), Siddhartha Academy of Higher Education (Deemed to be University), Vijayawada, India
Emails: hajiyev.h.a@mail.ru, e.a.hajiyev@yandex.ru, ilkhamova-z@mail.ru, klochko.e.n@yandex.ru, elaxmi2002@yahoo.com
Abstract
In decision-making, neutrosophic set allows for the information representation with three membership functions: truth (T), indeterminacy (I), and false (F). Each component in a neutrosophic set has membership, non-membership, and indeterminacy degrees that are independent and range from 0 to 1. This makes neutrosophic set especially suitable in complex decision-making scenarios where information is contradictory, incomplete, or ambiguous, which enables robust and more nuanced analysis and solutions. A large portion of finance companies experience problems handling vast amounts of data. These data are often left unstructured and unorganized. Therefore, it is necessary to classify them to exploit it. Data classification also simplifies to use, locating, and retrieval of information. It becomes vital while handling risk management, legal discovery, data security, and compliance. Therefore, this manuscript presents an Intuitionistic Fuzzy-Valued Neutrosophic Multiset based Financial Data Classification (IFVNMS-FDC) technique in Complex Systems. The main aim of the IFVNMS-FDC technique is to recognize and categorize the financial data into respective classes. To do so, the IFVNMS-FDC technique initially uses min-max scalar as a pre-processing step. Besides, the high-dimensional financial data can be handled by the design of whale optimization algorithm (WOA) based feature selection. Finally, the IFVNMS-FDC technique derives IFVNMS technique for the identification of various classes related to the financial data. A wide-ranging experiments were involved in exhibiting the performance of the IFVNMS-FDC technique. The experimental values depicted that the IFVNMS-FDC method obtains reasonable performance on financial data recognition.
Keywords: Financial Data Classification; Neutrosophic Logic; Whale Optimization Algorithm; Intuitionistic Fuzzy Set; Intuitionistic Fuzzy Value