| An importance of the idea of granularity of knowledge for approximate reasoning has been recently stressed in 6,9-10]. We address here the problem of synthesis of adaptive decision algorithms and we propose an approach to this problem based on the notion of a granule which we develop in the framework of rough mereology. This framework does encompass both rough and fuzzy set theories. Our appr...

Many learning methods ignore domain knowledge in synthesis of concept approximation. We propose to use hierarchical schemes for learning approximations of complex concepts from experimental data using inference diagrams based on domain knowledge. Our solution is based on the rough set and rough mereological approaches. The effectiveness of the proposed approach is performed and evaluated on art...

The medical field is considered as one of the most significant research resources. It receives a significant interest from researchers in the field of informatics and medical experts. It has a tremendous amount of data on various diseases and their symptoms that causes difficulty in diagnosing diseases. Therefore, several medical approaches based on knowledge discovery in the database have been...

Rough set theory is a paradigm for approximate reasoning based on the assumption that concepts are divided into exact and non–exact (called also rough) ones by means of a topological structure induced by a representation of knowledge as a classification. A classification in its most simple form is an equivalence relation on a universe of objects; the classification induces a partition topology ...

Rough set theory was proposed by Pawlak [1] as a mathematical tool to handle imprecision and uncertainty in data analysis. It has been successfully applied tomachine learning, intelligent systems, inductive reasoning, pattern recognition, mereology, image processing, signal analysis, knowledge discovery, decision analysis, expert systems, and many other fields [2–5]. The basic structure of roug...

Granular computing has been applied in many fields to solve problems and describe many spaces at different granularity and hierarchies. This paper proposes a rule generation approach based on granular computing in the frame of rough mereology. The proposed approaches generates a single rule granule from granular space in each step instead of selecting a suitable attribute according to some meas...

We are concerned with formal models of reasoning under uncertainty. Many approaches to this problem are known in the literature e.g. Dempster-Shafer theory, bayesian-based reasoning, belief networks, fuzzy logics etc. We propose rough mere-ology as a foundation for approximate reasoning about complex objects. Our notion of a complex object includes approximate proofs understood as schemes const...

This article presents a Multi-Agent approach for handling the problem of recommendation. The proposed system works via two main agents; namely, the matching agent and the recommendation agent. Experimental results showed that the proposed rough mereology based Multi-agent system for solving the recommendation problem is scalable and has possibilities for future modification and adaptability to ...

Part One of this paper is a case against classical mereology and for Heyting mereology. This case proceeds by first undermining the appeal of classical mereology and then showing how it fails to cohere with our intuitions about a measure of quantity. Part Two shows how Heyting mereology provides an account of sets and classes without resort to any nonmereological primitive.