Conformal Predictors for Compound Activity Prediction
Abstract
The paper presents an application of Conformal Predictors to a chemoinformatics problem of identifying activities of chemical compounds. The paper addresses some specific challenges of this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures (NCM) extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data. Keywords: Conformal Prediction, Confidence Estimation, Chemoinformatics, Non-Conformity Measure.
Cite
@article{arxiv.1603.04506,
title = {Conformal Predictors for Compound Activity Prediction},
author = {Paolo Toccacheli and Ilia Nouretdinov and Alexander Gammerman},
journal= {arXiv preprint arXiv:1603.04506},
year = {2016}
}
Comments
17 pages, 5 figures