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
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