Abstract
Nowadays, under the pressure of numerous research publications, researchers increasingly pay attention to writing survey papers to track and understand one research topic they are interested in, and then begin to conduct more in-depth research. Until this moment, there are two types of survey papers: traditional review analysis and bibliometric statistical analysis. Compared with traditional review analysis, due to the analysis of various bibliometric information that can be quickly summarized to assess and predict one research field's development, the bibliometric statistical analysis is progressively proposed. However, no research relied on the bibliometric approach to explore fuzzy inference system (FIS) -based classifiers. More importantly, since the current open-ended bibliometric analysis approaches have different assessment focuses, choosing a suitable approach is problematic. Therefore, based on the extraction, integration, and expansion of previous bibliometric analysis theories, this research proposes a new systematic and time-saving bibliometric statistical analysis approach. It is worth noting that the proposed approach eliminates the need to read the internal content of all publications. Two core parts (Publication Information and TOP 20 SETs) are generated by the proposed analysis approach. Among them, analyzing Author Keywords and TOP 20 SETs are unprecedented guiding features to assist researchers in exploring research topic. Significantly, this research relies on the proposed approach to explore FIS-based classifiers. Various assessments cover the bibliometric information of the entire related publications. In addition, these results may need to be considered to increase the citation rate of future publications.
Highlights
Classification is one fundamental research task within developing the techniques of Data Mining and Artificial Intelligence
Since mapping the feature of a sample data to a set of category labels is the core task of classifiers, the Fuzzy Inference System (FIS) gradually attracted the researchers’ attention as a proven universal approximation [1]
FIS-based classifiers became an alternative structure for designing flexible classifiers, together with Bayesian classifiers, Decision Trees, Neural Network-based classifiers, and Support Vector Machines [3]
Summary
Classification is one fundamental research task within developing the techniques of Data Mining and Artificial Intelligence. Evolvable FIS-based classifiers’ core advantages include organizing and updating their structure and parameters in real-time and online They were generally associated with data stream processing and approximated a dynamically changing environment [4]. In the current research cases, reference [21] used bibliometric analysis to study WoS’s m-learning publications It provided readers with the commonly systematic statistical information to deepen their understanding. The proposed approach extracts and summarizes the most relevant research information and resources that have a significant impact on the research topic (FIS-based Classifiers). Two well-known databases, WoS and SCOPUS, are used to extract all bibliometric information of FIS-based classifiers’ publications. The ‘‘SUMMATION & FUTURES’’ section proposes a summary of the analysis results and points out the future works about the proposed approach
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