SVM ensemble classification of nmr spectra based on different configurations of data processing techniques

The early detection of drug-induced organ toxicities is one of the major goals in safety pharmacology. Automating this process by classification of metabolic changes based on the analysis of 1H nuclear magnetic resonance spectra improves this process. In this paper we propose an ensemble classification system based on support vector machines trained on diverse ldquoviewsrdquo on the data. These views are created by variation of preprocessing techniques and the final classification is achieved by voting on an optimized selection of all experts. Results of an experimental evaluation on a challenging data-set from industrial safety pharmacology show the effectiveness of the proposed approach w.r.t. the detection of drug-induced toxicity.