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- W2298227532 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." @default.
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- W2298227532 date "2016-01-01" @default.
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- W2298227532 title "Conformal Predictors for Compound Activity Prediction" @default.
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- W2298227532 doi "https://doi.org/10.1007/978-3-319-33395-3_4" @default.
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