What is model integration in data fusion

1. MODEL INTEGRATION
MAY 2019
Seyed Faridoddin Kiaei
sfd.kiaei@ut.ac.ir

2. Problem Definition
Fusion issue
Data & Feature
Integration
Decision
Integration
Model
Integration
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3. Model in Software engineering (1)
 A model can refer to a component or system
 Technically, individual models are designed to serve as stand-alone components that serve unique
purposes and goals
 At the technical level, models should be able to ‘talk to each other’
 At the semantic level, models should 'understand each other’
Reference:An overview of the model integration process: From pre-integration assessment to testing (2016)
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4. Model in Software engineering (2)
 Models are simplifications of reality
 and are developed with the objective to understand a concept or system,
 to analyze what its future states and trends may look like
Reference:An overview of the model integration process: From pre-integration assessment to testing (2016)
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5. Model in Software engineering (3)
 Integration of models assumes linking such heterogeneous models together into an operational model
chain (Knapen et al., 2013), or rather a network with loops and feedbacks, where one model down the
chain can also feed input back into a model above.
 Based on this we define a model integration framework as a set of software libraries, classes, and
components that enable one to manage technical, semantic, and dataset aspects of interoperability.
 Integration of models requires mediation that goes beyond merging information and data that use
different schemas.
Reference: Designing the Distributed Model Integration Framework e DMIF (2017)
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6. Model integration phases
Reference:An overview of the model integration process: From pre-integration assessment to testing (2016)
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7. Model in Mechanical engineering
 The integration of model and data is a process of assembling heterogeneous tools and methods to
generate new knowledge that is meaningful and useful for particular engineering tasks.
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8. Example
 Stacking-based ensemble learning method
 Two level models:
 Base learners for preliminary predicting the posteriori class probabilities of samples (model selection)
[ensemble pruning]
 Meta-learner for predicting the final class label by combining the base learners (model combination)
Reference: Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection (2019)
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9. Example
Base-level learning module
Reference: Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection (2019)
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10. Example
Multi-objective evolutionary module
 Multi-objective evolutionary module depicts the techniques of model selection and model
combination
Reference: Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection (2019)
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11. Example
Multi-objective evolutionary module
Reference: Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection (2019)
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12. Example
Model combination by stacking
 The most common method is majority voting and its variants
 This paper utilized the stacked generalization scheme in conjunction with multi-objective optimization
algorithm for constructing ensembles
 According to the Wolpert’s idea of stacked generalization, the outputs of an ensemble can serve as the
inputs to a second-level meta-learner to learn the mapping between the outputs of the base learners
and the real class label (link). The intention of stacking is to explore a better approach to combine the
trained base learners.
 Random Forest in this system used as stacking combiner (meta-learner)
Reference: Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection (2019)
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13. Another example
 Apply k-nearest-neighbor (kNN), support vector machines (SVMs), decision trees (DTs), random
forests (RFs), and gradient boosting decision trees (GBDTs) as first-stage classification models
Reference:A deep learning-based multi-model ensemble method for cancer prediction (2018)
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14. Another example (cont.)
 Multi-model ensemble is a technique in which the predictions of a collection of models are given as
inputs to a second-stage learning model
 The second-stage model is trained to combine the predictions from first stage models optimally to
form a final set of predictions (e.g. OWA)
 Here, deep learning is the ensemble model to stack the multiple classifiers
Reference:A deep learning-based multi-model ensemble method for cancer prediction (2018)
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15. Another example (cont.)
Reference:A deep learning-based multi-model ensemble method for cancer prediction (2018)
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16. Conclusion
 Model Integration is usually used in software engineering or other problems which has heterogeneous
tools and must communicate with each other to reach a decision.
 But in Bioinformatics we have homogeneous data and we don’t need defining communication protocol
for different classifiers.
 Sometimes Model Integration and Decision Integration used interchangeably, but both of them are
trying to do the same task which is making a better decision from different classifiers decisions. To the
best of my knowledge, the only difference is that in Model Integration, finally, we have an integrated
model, but in the other one, we have a final decision.
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17. THANKYOU