# Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes
## 3 Groundwork of eFES  
This section presents background on the underlying theory, mathematical constructs, definitions, algorithms, and the framework. The elevated-level framework shown in Figure 3 elaborates on the importance of each definition, incorporated with the other units of eMLEE and the ability to implement parallel processing by design. In general computing, parallel processing is done by dividing program instructions to be run by multiple processors, so the time efficiency can be improved. This also ensures the maximum utilization of otherwise idle processors. Similar concepts can be implemented on the algorithms and ML models. ML algorithms depend on the problem and data types and require sequential training of each of the data models. However, the parallel processing can dramatically improve the learning process, especially for the blended model, such as eMLEE. In light of the latest work of parallel processing in ML, such as in [PERSON] _et al._ (2018), the authors introduced the parallel framework on ML algorithms for large graphs. They experimented with aggregation and sequential steps in their model to allow researchers to improve the usage of various algorithms. Another study was done in [PERSON] _et al._ (2018), where authors used induction to improve the parallelism in the decision trees. Python and R libraries have come a long way to help provide useful libraries and classes to develop various ML techniques. Authors in [PERSON] _et al._ (2018) introduced a python library Qjan to parallelize the ML algorithms in compliance by MapReduce. A PhD thesis [PERSON] _et al._ (2018) work done by a student at the University of California at Berkeley used concurrency control method to parallelize the ML process. Another study done in [PERSON] _et al._ (2018) utilized parallel processing approaches in ML techniques for detection in big-data networks. Therefore, similar progresses have motivated us to incorporate parallel processing in the proposed model. Our proposed model parallelism is done in two layers:  
1. Outer layer to eFES, where eFES unit communicates with other units of the eMLEE such as eABT, eWPM, eCVS and LT. Parallelism is done through real-time metric measurement with LT object and based on classifier learning, eFES reacts to the inner layer (defined next). Other units such as eABT and eCVS enhance the algorithm blend and test-training split in parallel, while eFES is being trained. In other words, all four units including eFES regulated by LT unit, are run in parallel to improve the speed of the learning process and validation for every feature as processed in the eFES unit and every algorithm processed in the eABT unit. However, eFES can also work without being related to the other units, if researchers and industrialists may however choose so. 2. Inner layer to eFES, where adding and removing of the feature are done in parallel. When the qualifying feature is added, the metrics are measured by the model to see if fitness improves, and then features are added and removed one by one to see the effect on the fitness function. This may be done sequentially, but parallelism improves the insurance that each feature is evaluated at the same time; the classifier is incorporating metrics reading from LT object and speed of the process especially when the huge dataset is being processed. Figure 3 illustrates the inner layer parallel processing of each construct that constitutes the eFES unit. It shows the high-level block diagram of eFES unit modeling and related functions. Each definition is explained in plain English next. Definition 1 covers the theory of LT unit, which works as a main coordinator assisting and regulating all the sub-units of eMLEE engine such as eFES. It inherently is based on parallel processing at a low level. While the classifier is in the learning process, LT object (in parallel) performs the measurements, records it, updates it as needed, and then feeds the learning process back.