@article{0c932c9a742746428662a1ff97ebdbaa,
title = "Prediction of the adsorption capability onto activated carbon of a large data set of chemicals by local lazy regression method",
abstract = "Accurate quantitative structure-property relationship (QSPR) models based on a large data set containing a total of 3483 organic compounds were developed to predict chemicals' adsorption capability onto activated carbon in gas phrase. Both global multiple linear regression (MLR) method and local lazy regression (LLR) method were used to develop QSPR models. The results proved that LLR has prediction accuracy 10% higher than that of MLR model. By applying LLR method we can predict the test set (787 compounds) with Q2ext of 0.900 and root mean square error (RMSE) of 0.129. The accurate model based on this large data set could be useful to predict adsorption property of new compounds since such model covers a highly diverse structural space.",
keywords = "Activated carbon adsorption capability, Genetic algorithm (GA), Local lazy regression (LLR), Quantitative structure-property relationship (QSPR)",
author = "Beilei Lei and Yimeng Ma and Jiazhong Li and Huanxiang Liu and Xiaojun Yao and Paola Gramatica",
note = "Funding Information: This work was supported by the Program for New Century Excellent Talents in University (Grant No. NCET-07-0399 ), the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars and the National Natural Science Foundation of China ( J0730425 ). ",
year = "2010",
month = aug,
doi = "10.1016/j.atmosenv.2010.05.021",
language = "English",
volume = "44",
pages = "2954--2960",
journal = "Atmospheric Environment",
issn = "1352-2310",
publisher = "Elsevier Ltd.",
number = "25",
}