Thanks Supun! Initially I thought to have same weights, but excellent suggestion on accuracy based weights.
On Thu, Sep 24, 2015 at 9:45 PM, Supun Sethunga <[email protected]> wrote: > +1 for the idea, and looks very feasible! > > May be we need to decide on a voting criteria, if we already don't have > any, such as whether to assign similar weights to all the classifiers, or > to assigns weights on their accuracy at the validation phase, etc.. > > Thanks, > Supun > > > On Thu, Sep 24, 2015 at 11:54 AM, Nirmal Fernando <[email protected]> wrote: > >> Hi All, >> >> In statistics and *machine learning*, *ensemble* methods use multiple >> *learning* algorithms to obtain better predictive performance that could >> be obtained from any of the constituent *learning* algorithms. >> >> We thought of implementing ensemble in CEP-ML extension. CEP-ML extension >> will be initialized using a list of ML model paths. When an event is >> received, CEP-ML extension will perform predictions using all the models >> and output the majority vote. >> >> We can implement the same, in ESB-ML extension. >> >> Thoughts are welcome! >> >> >> ---------- Forwarded message ---------- >> From: Manorama Perera <[email protected]> >> Date: Thu, May 14, 2015 at 3:35 PM >> Subject: CEP Extension for Machine Learner Predictions >> To: architecture <[email protected]> >> Cc: Nirmal Fernando <[email protected]>, Srinath Perera <[email protected]>, >> Supun Sethunga <[email protected]>, Upul Bandara <[email protected]>, >> Sriskandarajah Suhothayan <[email protected]>, Maheshakya Wijewardena < >> [email protected]> >> >> >> Hi, >> >> We are in the process of implementing a CEP extension for Machine Learner >> Predictions. This extension allows the machine learning models generated by >> WSO2 ML to be used within CEP for predictions. >> >> To use this, following ML features need to be installed in CEP. >> >> - Machine Learner Core feature >> - Machine Learner Commons feature >> - Machine Learner Database Service feature >> >> This extension is implemented as a *StreamProcessor*. >> >> *The syntax :* >> >> There are two possible ways to use the extension. >> >> *<stream-name>#ml:predict(‘<path-to-ML-model>’) * >> >> *<stream-name>#ml:predict('<path-to-ML-model>', attribute 1, attribute 2, >> .......)* >> >> *path-to-MLModel* >> >> The storage location of the Machine learning model can be either registry >> or file system. >> >> If the model is stored in the registry, *path-to-ML-model* should have >> the prefix *registry:* >> If the model is stored in the file system, *path-to-ML-model* should >> have the prefix *file:* >> >> *attribute 1, attribute 2, ….* >> >> These are the attribute names of the stream. The values of these >> attributes are sent to the MLModel as feature input values. When the >> attribute names are not explicitly given, the extension will map the >> attribute names of the stream with the feature names of the ML model. >> >> The output events will contain the attribute* prediction* which holds >> the prediction result for that particular event. >> >> Thanks. >> >> -- >> Manorama Perera >> Software Engineer >> WSO2, Inc.; http://wso2.com/ >> Mobile : +94716436216 >> >> >> >> -- >> >> Thanks & regards, >> Nirmal >> >> Team Lead - WSO2 Machine Learner >> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >> Mobile: +94715779733 >> Blog: http://nirmalfdo.blogspot.com/ >> >> >> > > > -- > *Supun Sethunga* > Software Engineer > WSO2, Inc. > http://wso2.com/ > lean | enterprise | middleware > Mobile : +94 716546324 > -- Thanks & regards, Nirmal Team Lead - WSO2 Machine Learner Associate Technical Lead - Data Technologies Team, WSO2 Inc. Mobile: +94715779733 Blog: http://nirmalfdo.blogspot.com/
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