We propose a new ensemble classification algorithm, named super random subspace ensemble (Super RaSE), to tackle the … sparse classification problem. The proposed algorithm is motivated by the random subspace ensemble algorithm (RaSE). The RaSE … method was shown to be a flexible framework that can be coupled with any existing base classification. However, the success …

In the context of binary classification with continuous predictors, we proove two properties concerning the connections …

voting ensemble method with a decision tree (DT) with experimental data on US restaurant between 1980 and 2017. According to … the voting ensemble of business failure prediction. Three models, including an entire period (EP) model, an economic … prediction models. And second, by using an ensemble model with a DT, it has improved both the interpretability of the results and …

technologies. We integrate patent big data for technological integration and use text mining, boosting, and ensemble for integrated … technologies and analyze the integrated patent data using regression trees, random forest, extreme gradient boosting, and ensemble …

not linked to the error rate, the primary interest in many applications of classification. By introducing an upper bound … for the error rate a criterion is developed which can improve the classification performance. …

existing research frameworks, leading to a classification framework that can help structuring this emerging field. …