A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity

Abstract

There is a wide variety of studies that propose different classifiers to solve a large amount of problems in distinct classification scenarios. The No Free Lunch theorem states that if we use a big enough set of varied problems, all classifiers would be equivalent in performance. From another point of view, the performance of the classifiers is dependant of the scope and properties of the datasets. In this sense, new proposals on the topic often focus on a given context, aiming at improving the related state-of-the-art approaches.Data Complexity Metrics have been traditionally used to determine the inner characteristics of datasets. This way, researchers are able to categorise the problems in different scenarios. Then, this taxonomy can be applied to determine inner characteristics of the datasets in order to determine intervals of good and bad behaviour for a given classifier.In this work we will take advantage of the Data Complexity Metrics in order to design a fuzzy meta-classifier. The final goal is to create decision rules based on the inner characteristics of the data to apply a different version of the fuzzy classifier for a given problem. To do so, we will make use of the FARC-HD classifier, an Evolutionary Fuzzy System that has led to different extensions in the specialized literature. Experimental results show the goodness of this novel approach as it is able to outperform all versions of FARC-HD on a wide set of problems, and obtain competitive results (in terms of performance and interpretability) versus two selected state-of-the-art rule-based classification system, C4.5 and FURIA.

Power Incomplete Data Clustering Based on Fuzzy Fusion Algorithm

“…Lines 4 and 7 show that the transformers with equipment model numbers 6 and 8 are prone to oil seepage/leakage. Similarly, during the excavation process, it was found that almost every type of transformer had more or less oil seepage/leakage [23]. The analysis of strong association rules is shown in Table 6.…”

“…The location is divided into 14 categories according to the source administrative region of the incomplete data of the current analysis of power equipment, which are A7_1-A7_14. The final index nature is divided into 3 categories according to the Classification Standard for Primary Power Transmission and Transformation Equipment [23]. The standardization numbers are A8_1-A8_3, respectively, as shown in Table 3.…”

Fuzzy logic and proportional integral derivative based multi-objective optimization of active suspension system of a 4×4 in-wheel motor driven electrical vehicle

“…In the history of active suspension control applications, many approaches have been studied by researchers to improve comfort and ride handling (Ding et al, 2020; Li et al, 2020; Liu et al, 2019; Konoiko et al, 2019; Talib and Darus 2017; Vahedi and Jamali 2021). As a result of the conflicting nature of performance requirements such as comfort, handling, and suspension displacement, a variety of control strategies such as linear quadratic regulator (LQR) (Sam et al, 2000), adaptive sliding control (Chen and Huang 2005), H∞ and robust H ∞ control (Du and Zhang 2007), sliding mode control (SMC) (Akbari et al, 2010), FL (Cózar et al, 2019; Ejegwa et al, 2021; Elbab et al, 2009; Xu et al, 2021), preview control (Oraby et al, 2007), proportional integral (PI) control (Yildiz and Kopmaz 2017), optimal control (Marzbanrad et al, 2002, 2003), and neural network methods (Al-Holou et al, 2002) have been studied to handle with the trade-off performance criteria for active suspension control. Nonetheless, by their nature, some of these methods are resource-intensive and may under-perform in unfamiliar conditions.…”

A Novel Approach for Mining Big Data Using Multi-Model Fusion Mechanism (MMFM)

“…Using exact classification techniques developed [9], Access, querying, mining, and information retrieval problems are lessened. Elaggoune et al [10] used neural learning to categorize data according to the attributes connected to it. Similar to this suggestion, classification is modified using batch processing devices and distributed systems to address the problems with excessive resource consumption.…”