Conformal Predictors for Compound Activity Prediction

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationConformal and Probabilistic Prediction with Applications
Subtitle of host publication5th International Symposium, COPA 2016 Madrid, Spain, April 20–22, 2016 Proceedings
Number of pages16
ISBN (Electronic)978-3-319-33395-3
ISBN (Print)978-3-319-33394-6
Publication statusPublished - 17 Apr 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743

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