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The three-way decisions strategy has been employed to construct networktopology in a single hidden layer feedforward neural network (SFNN). However,this model has a general performance, and does not consider the process costs,since it has fixed threshold parameters. Inspired by the sequential three-waydecisions (STWD), this paper proposes STWD with an SFNN (STWD-SFNN) to enhancethe performance of networks on structured datasets. STWD-SFNN adoptsmulti-granularity levels to dynamically learn the number of hidden layer nodesfrom coarse to fine, and set the sequential threshold parameters. Specifically,at the coarse granular level, STWD-SFNN handles easy-to-classify instances byapplying strict threshold conditions, and with the increasing number of hiddenlayer nodes at the fine granular level, STWD-SFNN focuses more on disposing ofthe difficult-to-classify instances by applying loose threshold conditions,thereby realizing the classification of instances. Moreover, STWD-SFNNconsiders and reports the process cost produced from each granular level. Theexperimental results verify that STWD-SFNN has a more compact network onstructured datasets than other SFNN models, and has better generalizationperformance than the competitive models. All models and datasets can bedownloaded from https://github.com/wuc567/Machine-learning/tree/main/STWD-SFNN.

Sequential three-way decisions with a single hidden layer feedforward neural network