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Simplicity, efficiency and precision are basic principles for modeling and analyzing of coal-fired boilers data. However, the load fluctuations, system delay, multi-variable coupling pose great challenges to the high-precision modeling for NOx emission. A hybrid feature selection method for boiler data is proposed to predict accurately NOx emission. Firstly, the mechanism analysis is used to narrow the feature scope, and the feature set is selected preliminarily. Secondly, maximum information coefficient (MIC) method is introduced to calculate the correlation between features and NOx information to eliminate boiler system delay. Thirdly, a combined feature evaluation method is developed, which integrates Filter and Embedded method to obtain feature ranking, then the ranking information is regarded as priori knowledge to improve the genetic algorithm. Finally, a fitness function to maximize prediction accuracy and minimize feature dimension is constructed based on hybrid method to realize feature subset selection and NOx emission prediction. Original data are collected from one 1000 MW coal-fired unit in the Guangdong province of China. Experimental results show that the number of features is reduced by nearly 70%, the MAPE of LightGBM regression model is no more than 2%. Higher prediction accuracy can be obtained using the features extracted through the proposed hybrid method. [ABSTRACT FROM AUTHOR]

Best subset of groups selection (BSGS) is the process of selecting a small part of nonoverlapping groups to achieve the best interpretability on the response variable. It has attracted increasing attention and has far-reaching applications in practice. However, due to the computational intractability of BSGS in high-dimensional settings, developing efficient algorithms for solving BSGS remains a research hotspot. In this paper, we propose a group-splicing algorithm that iteratively detects the relevant groups and excludes the irrelevant ones. Moreover, coupled with a novel group information criterion, we develop an adaptive algorithm to determine the optimal model size. Under certain conditions, it is certifiable that our algorithm can identify the optimal subset of groups in polynomial time with high probability. Finally, we demonstrate the efficiency and accuracy of our methods by comparing them with several state-of-the-art algorithms on both synthetic and real-world data sets. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms-Discrete. Funding: This work was supported by National Natural Science Foundation of China [Grants 72171216, 71921001, and 71991474], the Key Research and Development Program of Guangdong [Grant 2019B020228001], the Science and Technology Program of Guangzhou, China [Grant 202002030129], The Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China [Grant 22XNH161], and the Outstanding Graduate Student Innovation and Development Program of Sun Yat-Sen University [Grant 19lgyjs64]. Supplemental Material: The online appendix and video are available at https://doi.org/10.1287/ijoc.2022.1241. [ABSTRACT FROM AUTHOR]