BGFS: Design and Development of Brain Genetic Fuzzy System for Data Classification

Abstract

Recently, classification systems have received significant attention among researchers due to the important characteristics and behaviors of analysis required in real-time databases. Among the various classification-based methods suitable for real-time databases, fuzzy rule-based classification is effectively used by different researchers in various fields. An important issue in the design of fuzzy rule-based classification is the automatic generation of fuzzy if-then rules and the membership functions. The literature presents different techniques for automatic fuzzy design. Among the different techniques available in the literature, choosing the type, the number of membership functions, and defining parameters of membership function are still challenging tasks. In order to handle these challenges in the fuzzy rule-based classification system, this paper proposes a brain genetic fuzzy system (BGFS) for data classification by newly devising the exponential genetic brain storm optimization. Here, membership functions are optimally devised using exponential genetic brain storm optimization algorithm and rules are derived using the exponential brain storm optimization algorithm. The designed membership function and fuzzy rules are then effectively utilized for data classification. The proposed BGFS is analyzed with four datasets, using sensitivity, specificity, and accuracy. The outcome ensures that the proposed BGFS obtained the maximum accuracy of 88.8%, which is high as compared with the existing adaptive genetic fuzzy system.

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A Hybrid Systematic Review Approach on Complexity Issues in Data-Driven Fuzzy Inference Systems Development

“…An essential issue in the design of fuzzy rule-based classification is the automatic generation of fuzzy if-then rules and MFs (Ravi and Khare, 2018), i.e. choosing the type of MFs, the number of MFs, and defining the parameters of MFs.…”

“…GaneshKumar et al, 2014), and comparison of fuzzy numbers (Matarazzo and Munda, 2001). As the selected papers' analysis shows, this issue is analysed in tandem with complexity issue of fuzzy rules, like in Rajeswari and Deisy (2019), Ravi and Khare (2018), Barsacchi et al (2019).…”

“…7) estimation method. In Ravi and Khare (2018), MFs and rules are devised using an exponential genetic brainstorm optimization algorithm. Authors of Altilio et al (2018) have used the Adaptive Neuro-FIS to optimize the generalization capability of the resulting model, i.e.…”

“…10a). The analysis of abstracts shows that major issues that affect the accuracy of FIS are fixing appropriate MFs and validating fuzzy rules before inferencing, like extracting outliers in (Rajeswari and Deisy, 2019), classification in Ravi and Khare (2018), Almasi and Rouhani (2016), inferencing in Altilio et al (2018), etc. The main issues related to data and datasets are noise (Ramathilaga et al, 2014) and imbalance (Chaudhuri, 2014).…”