This week, I am working to get the dynamic time warping functionality into my program. The process of doing so includes re-processing the features to include the time series, putting each series back together when we construct sequences, and then performing the DTW to generate a number that will be used to compute the kNN of each sequence which can then be used for predictions with the models. The processing time of these activities has gone up significantly since we have been using five different metrics with each of the F phase datasets. I am returning to school next week, and once I've completed the DTW processing all that will remain before we put together our second paper (The date for the reach journal we would like to submit it to is October 1), I am hoping I will have time to look again into the Agglomerative Hierarchical Clustering concept, which I did not successfully complete when we explored it earlier in the summer and then changed focus to the paper. We heard back...
This week, we are going to merge the “P” approach discussed
last week with a kNN grouping to generate sets of similar instances that can be
used to create a custom regression model for an individual instance. This introduces
several new variables in addition to k and the set of attributes used as
nearness indicators, as I discussed in Week 35, as well as the P value.
Since we are now using kNN as a means to make groups, we are
able to try out different regressors to generate models from these groups. The
models we will use in our experiments wil be decision trees, support vector
models, random forest models, bayes models and linear regression models. We
will use the packages in scikit learn to easily switch between regressors.
We will also vary the subgroupings used as the pool from
which kNN are selected, as we did with the original kNN model. These results
will be compared to those obtained by generating a model with the same
regressor, subgrouping and P value from all other epochs.
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