On integrating fuzzy knowledge using a Novel Evolutionary Algorithm

Abstract

Fuzzy systems may be considered as knowledge-based systems that incorporates human knowledge into their knowledge base through fuzzy rules and fuzzy membership functions. The intent of this study is to present a fuzzy knowledge integration framework using a Novel Evolutionary Strategy (NES), which can simultaneously integrate multiple fuzzy rule sets and their membership function sets. The proposed approach consists of two phases: fuzzy knowledge encoding and fuzzy knowledge integration Four application domains, the hepatitis diagnosis, the sugarcane breeding prediction, Iris plants classification,and Tic-tac-toe endgame were used to show the performance of the proposed knowledge approach.Results show that the fuzzy knowledge base derived using our approach performs better than Genetic Algorithm based approach.

An overview on consensus‐based approaches to processing collective inconsistency and knowledge integration

“…Since the diversity in the source of knowledge as well as the diversity in the representation of knowledge, one cannot propose a method that can be used for all knowledge integration tasks. In the current literature, many approaches to knowledge integration have been proposed such as genetic algorithm (Kim et al, ), evolutionary strategies (Chowdhury et al, ), negotiation (dos Santos & Bazzan, ), consensus methodology (Nguyen, ). According to Grant (), the mechanisms that can be used for knowledge integration within organizations including sequencing; the rules and directives (i.e., communications, manuals, directives, policies, and procedures); communication independent routines; unit‐based problem‐solving and decision‐making.…”

“…Referring to Chowdhury et al (), the authors presented a fuzzy knowledge integration framework using a novel evolutionary strategy (NES). This framework consists of two phases of fuzzy knowledge encoding and fuzzy knowledge integration that can serve for simultaneously integrating multiple fuzzy rule sets and their membership functions.…”

“…In other words, it is an integration of individual knowledge states originating from a number of members concerning a common subject in the real world. In the current literature, there exist many approaches to knowledge integration such as genetic algorithm (Kim, Han, & Lee, ), evolutionary strategies (Chowdhury, Khatun, & Hashem, ), negotiation (dos Santos & Bazzan, ), consensus methodology (Nguyen, ). To date, consensus choice has been proven useful in solving inconsistency of knowledge arising in the integration process with different representations of knowledge such as linear orders, semi‐lattices, n‐tree, ordered partitions and coverings, weak hierarchies, time intervals, ontologies (Day, ; Nguyen, ).…”

Liver disorder diagnosis using linear, nonlinear and decision tree classification algorithms

“…CBR-GA was used by Park et al (2011) to find total misclassification cost of CSCBR in hepatitis patient's records [40]. FL-GA was used by Wang et al (1998) [41] and Chowdhury et al (2007) [42]; AIS-ANN-FL was used by Kahramanli and Allahverdi (2009) [43] and ANN-CBR-RBR was used by Obot and Uzoka (2009) [44] to diagnose hepatitis disease. ANN- [48][49][50][51][52].…”