Hi Nirmal, However, ensemble would need test data to decide on the weight given to each model ( am I correct?)
If that is the case, we have to do this in the ML, not CEP. I think also we need stats etc on accuracy. So we have to do this in ML anyway. Thanks Srinath On Thu, Sep 24, 2015 at 10:37 PM, Maheshakya Wijewardena < [email protected]> wrote: > Indeed we should include bagging and boosting for linear models for > bias-variance trade-off. But these methods only concern a single learner at > a given moment and trains on the same dataset by sampling. > Our most imminent concern is to create ensemble of models created with > different datasets (different, but same features). > > It would be ideal if we can get a bagging mechanism for dataset versions. > > On Thu, Sep 24, 2015 at 10:28 PM, Upul Bandara <[email protected]> wrote: > >> Looks like a nice idea to try. >> >> But I have a little concern regarding how this is going affect the >> performance of stream processing especially when we have some expensive >> algorithms as base learners. >> >> As an alternative, we can try bagging with less expensive algorithms. >> >> >> >> On Thu, Sep 24, 2015 at 9:54 PM, Maheshakya Wijewardena < >> [email protected]> wrote: >> >>> There are constrained optimization techniques to determine the optimal >>> convex combinations of weights which we can implement, but at the moment we >>> need to get the vanilla majority voting scheme implemented. >>> Moreover the weighting will be more important in numerical prediction. >>> >>> On Thu, Sep 24, 2015 at 9:48 PM, Nirmal Fernando <[email protected]> >>> wrote: >>> >>>> 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/ >>>> >>>> >>>> >>> >>> >>> -- >>> Pruthuvi Maheshakya Wijewardena >>> Software Engineer >>> WSO2 : http://wso2.com/ >>> Email: [email protected] >>> Mobile: +94711228855 >>> >>> >>> >> >> >> -- >> Upul Bandara, >> Associate Technical Lead, WSO2, Inc., >> Mob: +94 715 468 345. >> > > > > -- > Pruthuvi Maheshakya Wijewardena > Software Engineer > WSO2 : http://wso2.com/ > Email: [email protected] > Mobile: +94711228855 > > > -- ============================ Blog: http://srinathsview.blogspot.com twitter:@srinath_perera Site: http://people.apache.org/~hemapani/ Photos: http://www.flickr.com/photos/hemapani/ Phone: 0772360902
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