Comprehensive Ensemble is an multi-subject ensemble.
Instead of limiting the ensemble diversity to a single subject, it combines multi-subject individual models comprehensively; ensemble for the combinations of bagging, methods, and chemical compound input representations.
There exist a new type of QSAR individual classifier that is an end-to-end neural network model based on 1D-CNN and RNN. It extracts sequential features automatically from the SMILES.
A set of models are combines by using second-level combining learning (meta-learning), and meta-learning provides an interpretation regarding the importance of individual models through learned weights.
Our proposed ensemble learning procedure is divided into two levels: first-level individual learning and second-level combining learning. The first-level learning is a level for individual learning from diversified learning algorithms and chemical compound representations. The prediction probabilities from the first-level learning models are used as inputs for the second-level learning. The second-level combining learning makes the final decision by learning the importance of individual models from the first-level predictions.
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