Rough sets: past, present, and future

Area of Science:

• * Computational intelligence and data analysis.
• * Artificial intelligence and machine learning.
• * Mathematical foundations of computing.

Background:

• * Rough set theory, introduced by Professor Zdzisław Pawlak, marks 35 years of development.
• * The theory has garnered global attention from researchers and practitioners.
• * Rough set methods, individually or combined with other approaches, have found diverse applications.

Purpose of the Study:

• * To review selected past and present research directions in rough set theory.
• * To highlight the significance of searching strategies for approximation spaces.
• * To explore new challenges in problem-solving for intelligent systems (IS) and complex adaptive systems (CAS).

Main Methods:

• * Emphasizing the role of approximation spaces in discovering computational building blocks (granules/patterns).
• * Discussing interactive granular computing as an extension of rough set theory.
• * Focusing on strategies for effective computation realization in IS and CAS.

Main Results:

• * Identification of key research trends in rough set theory.
• * Underscoring the importance of approximation spaces for concept approximation.
• * Highlighting the potential of granular computing for advanced intelligent systems.

Conclusions:

• * Rough set theory continues to evolve, offering powerful tools for data analysis.
• * Interactive granular computing presents a promising avenue for tackling complex problems in IS and CAS.
• * These advancements are crucial for the progress of natural computing.