This study deals with classification for toxicity prediction. Using a data set of 235 pesticides and 153 descriptors, we built several models using seven classification algorithms: nearest mean classifier, linear discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, soft independent modeling of class analogy, K nearest neighbors classification, classification, and regression tree. The performance of the models was then compared with the classifier, the end-points, the number of descriptor, and the diversity of the data set. Finally, we made a critical analysis of the models and descriptors.
|Number of pages||8|
|Journal||Journal of Chemical Information and Computer Sciences|
|Publication status||Published - Jan 2004|
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics