Abstract
A rule-based system is a special type of expert system, which typically consists of a set of if–then rules. Such rules can be used in the real world for both academic and practical purposes. In general, rule-based systems are involved in knowledge discovery tasks for both purposes and predictive modeling tasks for the latter purpose. In the context of granular computing, each of the rules that make up a rule-based system can be seen as a granule. This is due to the fact that granulation in general means decomposition of a whole into several parts. Similarly, each rule consists of a number of rule terms. From this point of view, each rule term can also be seen as a granule. As mentioned above, rule-based systems can be used for the purpose of knowledge discovery, which means to extract information or knowledge discovered from data. Therefore, rules and rule terms that make up a rule-based system are considered as information granules. This paper positions the research of rule-based systems in the granular computing context, which explores ways of achieving advances in the former area through the novel use of theories and techniques in the latter area. In particular, this paper gives a certain perspective on how to use set theory for management of information granules for rules/rule terms and different types of computational logic for reduction of learning bias. The effectiveness is critically analyzed and discussed. Further directions of this research area are recommended towards achieving advances in rule-based systems through the use of granular computing theories and techniques.
Highlights
A rule-based system is a special type of expert system, which is made up of a set of rules, which typically takes the form of if– rules
Each set is known as a fuzzy set, which is due to the fact that each of the elements in such a set may only be given a partial membership to the Deterministic Logic Probabilistic Logic Fuzzy Logic Rough Logic
This paper introduced the theoretical preliminaries of rulebased systems and granular computing as well as argued the relationships between the two areas in several contexts: the theory of hierarchy, computational intelligence, artificial intelligence, divide and conquer and the theory of small groups
Summary
A rule-based system is a special type of expert system, which is made up of a set of rules, which typically takes the form of if– rules Such rules can be designed through the use of expert knowledge or through learning from real data. As introduced in Liu et al (2015a), rule-based systems can be used in practice for the purpose of knowledge discovery, which means to extract knowledge or information discovered from data In this context, rules, rule bases and rule terms are seen as information granules. On the basis of the above description, this paper proposes a technique through use of the rough set theory towards appropriate handling of missing values when their absence is on the artificial basis mentioned above. 4. Section 4 explores ways of achieving advances in rule-based systems through novel use of granular computing theories and techniques.
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