In recent years, imperialist competitive algorithm (ICA), genetic algorithm (GA), and hybrid fuzzy classification systems have been successfully and effectively employed for classification tasks of data mining. Due to overcoming the gaps related to ineffectiveness of current algorithms for analysing high-dimension independent datasets, a new hybrid approach, named HYEI, is presented to discover generic rule-based systems in this paper. This proposed approach consists of three stages and combines an evolutionary-based fuzzy system with two ICA procedures to generate high-quality fuzzy-classification rules. Initially, the best feature subset is selected by using the embedded ICA feature selection, and then these features are used to generate basic fuzzy-classification rules. Finally, all rules are optimized by using an ICA algorithm to reduce their length or to eliminate some of them. The performance of HYEI has been evaluated by using several benchmark datasets from the UCI machine learning repository. The classification accuracy attained by the proposed algorithm has the highest classification accuracy in 6 out of the 7 dataset problems and is comparative to the classification accuracy of the 5 other test problems, as compared to the best results previously published. 1. Introduction In general, fuzzy logic is closer to human logic; thus it can deal with real-world noise and imprecise data [1, 2]. Fuzzy models have several advantages. The most important advantage is that they have flexible decision boundaries, and thus, they are characterized by their higher ability to adjust to a specific domain of application and accurately reflect its particularities. A fuzzy model can be generated by describing a fuzzy-classification rule set and then improved by decreasing the length and the number of rules. This approach is a complex task, since several issues must be resolved for the fuzzy model which are produced. First, the basic fuzzy-classification rules must be well defined. In the optimization stage, the length of rules is reduced and when length of one rule equals zero then the mentioned rule will be removed from the rule set. The resulted rule set will be more interpretable since it has fewer number of rules. Several approaches have been suggested in the literature for the improvement of knowledge-based fuzzy models. In most of them, the model is trained using a recognized optimization technique (i.e., fuzzy rules with genetic algorithms [3]). In this work, we propose an algorithm for generating high-quality fuzzy-classification rules which includes three
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