adapting multicomponent predictive systems using hybrid adaptation strategies with auto-weka in...
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Adapting Multicomponent Predictive Systems using Hybrid Adaptation with Auto-WEKA
in Process Industry
Manuel Martín Salvador, Marcin Budka and Bogdan Gabrys{msalvador,mbudka,bgabrys}@bournemouth.ac.uk
Data Science Institute, Bournemouth University
AutoML @ ICML 2016New York, USAJune 24th, 2016
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Example of MCPS with parallel paths
dummy dummy
i o
Random Feature Selection
RandomSubspace
Decision Tree
Mean
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Maintaining an MCPS● Data distribution can change over time and affect predictions
○ External factors (e.g. weather conditions, new regulations)○ Internal factors (e.g. quality of materials, equipment deterioration)
Source: INFER project
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Training and testing process
1. Training data is provided
2. Best MCPS found is selected
3. New batch of unlabelled data requires prediction
4. MCPS generates predictions
5. True labels are provided
6. Predictive accuracy is reported
7. MCPS is adapted using the last batch of labelled data
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Evaluated strategies
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Datasets from chemical production processes
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Average classification error (%)
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Average classification error per batch (%)
BaselineBatchBatch+SMACCumulativeCumulative+SMAC
drierthermalox
Batch adaptation doesn’t help! :(
Batch adaptation does help! :)
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MCPS similarity analysis
Batch+SMAC Cumulative+SMAC
catalyst catalyst
Same components, only hyperparameters are adapted
Large difference between batches