Boosting Applied to Classification of Mass Spectral Data

10.6339/JDS.2003.01(4).173

K. Varmuza；Ping He；Kai-Tai Fang

Boosting ； data mining ； decision tree ； mass spectra

Journal of Data Science

1卷4期（2003 / 10 / 01）

391 - 404

英文

Boosting is a machine learning algorithm that is not well known in chemometrics. We apply boosting tree to the classification of mass spectral data. In the experiment, recognition of 15 chemical substructures from mass spectral data have been taken into account. The performance of boosting is very encouraging. Compared with previous result, boosting significantly improves the accuracy of classifiers based on mass spectra.