At present time, an effective tool becomes essential to forecast business failure as well as financial crisis on small- to medium-sized enterprises. This article presents a new optimal feature selection (FS)-based classification model for financial crisis prediction (FCP). The proposed FCP method involves data acquisition, preprocessing, FS, and classification. Initially, the financial data of the enterprises are collected by the use of the internet of things devices, such as smartphones and laptops. Then, the pigeon-inspired optimization (PIO)-based FS technique is applied to choose an optimal set of features. Afterward, the extreme gradient boosting (XGB)-based classification optimized by the Jaya optimization (JO) algorithm called JO-XGB is employed to classify the financial data. The application of the JO algorithm helps to tune the parameters of the XGB model. A detailed experimental validation process takes place to ensure the performance of the presented PIO-JO-XGBoost model. The obtained simulation results verified the effectiveness of the presented model over the compared methods.

中文翻译：

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物联网使用基于最优特征子集选择的分类模型实现企业金融危机预测

目前，一种有效的工具对于预测中小企业的业务失败和金融危机变得至关重要。本文提出了一种新的基于最优特征选择 (FS) 的金融危机预测 (FCP) 分类模型。提议的 FCP 方法涉及数据采集、预处理、FS 和分类。最初，企业的财务数据是通过使用智能手机和笔记本电脑等物联网设备来收集的。然后，应用基于鸽子启发优化 (PIO) 的 FS 技术来选择一组最佳特征。之后，采用名为 JO-XGB 的 Jaya 优化 (JO) 算法优化的基于极端梯度提升 (XGB) 的分类对金融数据进行分类。JO算法的应用有助于调整XGB模型的参数。进行了详细的实验验证过程以确保所呈现的 PIO-JO-XGBoost 模型的性能。获得的仿真结果验证了所提出模型相对于比较方法的有效性。