Multiple feature tree

Multiple Feature Tree Models Applications in HTS Data Analysis... 

For solving the pattern recognition problem encountered in the operation of chemical processes, the analysis of measured process data and extraction of process trends at multiple scales constitutes the feature extraction, whereas induction via decision trees is used for inductive... 

In a mixture-of-experts model (Jacobs et al., 1991), different expert networks were assigned to tackle sub-tasks of training cases, and an extra gating network was used to decide which of the experts should determine the output. The model discovered a suitable decomposition of the input space as part of the learning process. Later the model was further extended (Jordan Jacobs, 1994) into a hierarchical system with a tree structure. In molecular applications, cascaded networks-where outputs of some networks become the inputs of others-were used to improve performance (Rost Sander, 1994). Multiple neural network modules may run in parallel in order to scale up the system (Wu et al., 1995). More than one network can also be used to extract different (e.g., local vs. global) features (Mahadevan Ghosh, 1994). 

Multiple ensembles are generated by randomly repartitioning the feature space and building optimal trees. While CERP captures most of the features contained in the data, only a few selected variables are used by each tree classifier in C-T CERP or LR-T CERP. When we have multiple ensembles, fresh new information can be obtained by a different partition of the variables in each additional ensemble. The multiple ensembles contribute to a further gain of the overall accuracy. 

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