Conformal Predictors for Compound Activity Prediction
| Autor: | Paolo Toccaceli, Alexander Gammerman, Ilia Nouretdinov |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
Flexibility (engineering) FOS: Computer and information sciences business.industry Feature vector 05 social sciences 050801 communication & media studies Conformal map Mondrian Machine learning computer.software_genre Domain (software engineering) Machine Learning (cs.LG) Set (abstract data type) Computer Science - Learning 03 medical and health sciences Class imbalance 030104 developmental biology 0508 media and communications Cheminformatics Artificial intelligence business computer Mathematics |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783319333946 COPA Lecture Notes in Computer Science Lecture Notes in Computer Science-Conformal and Probabilistic Prediction with Applications COPA 2016: Conformal and Probabilistic Prediction with Applications |
| ISSN: | 0302-9743 1611-3349 |
| DOI: | 10.48550/arxiv.1603.04506 |
| Popis: | 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. Comment: 17 pages, 5 figures |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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