# Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes
## 2 Related Study
[20] discussed the process of purchase decision in subject minds using MRI scanning images through ML methods. Using the recursive cluster elimination-based SVM method, they obtained higher accuracy (71%) as compared to previous findings. They utilized Filter (GML) and wrapping methods (RCE) for feature selection. Their work also needs to be extended to other image techniques in healthcare. [PERSON] et al. [21] provided a survey on ML application for wearable sensors, based on human activity recognition. They provided a taxonomy of learning approach and their related response time on their experiments. Their work also supported feature extraction as an important phase of ML process. ML has also shown a promising role in engineering, mechanical, and thermo-dynamic systems. [PERSON] et al. [22] worked on ML techniques to do the prediction in the thermal systems for systems components. Besides many different units and technique adoptions, they also utilized FS methods based on correlation feature selection algorithm. They used Weka data-mining tools and came up with the reduced feature set of 16 for improved accuracy. However, their study did not reveal how exactly they came up with this number and whether different number of the features would have helped any further. [PERSON] et al. [23] used the supervised feature method to remove redundant features and considered the important ones for their gender classification. However, they used the neural network method as a feature extraction method, which is mostly common in unsupervised learning. Their work is yet to be tested for more computer vision tasks including image recognition tasks in which bimodal vein modeling becomes significant. [PERSON] et al. [24] utilized the concept of F-measure optimization for FS. They developed a cost-sensitive feature approach to determine the best F-measure-based feature for the selection by ML process. They argued F-measure to be better than accuracy, for purposes of performance measurement. However, accuracy is not sufficient to be considered a baseline for performance reflection of any model or process. [PERSON] et al. [PERSON] _et al._ (2018) proposed solutions for IoT-based feature models using the multi-objective optimum approach. They enhanced the binary pattern for nested cardinality constraints using three paths. The second path was observed to increase the time complexity due to the increasing group of features. Though their work was not directly in ML methodologies, their work showed performance improvement in the 3 rd path when the optional features were removed. Here are the gaps that we identified based on a comprehensive literature review and comparisons made, evaluated, and presented in Section 5 later in this paper. 1. Parallel processing of the features, in which features can be evaluated one by one, has not been done, while the model metrics are being measured and recorded simultaneously to see the real-time effect. 2. Improved grouping of features is needed across diverse feature types in datasets for improved performance and generalization. 3. 3D modeling is rarely done. The 3D approach can help for side-by-side metric evaluation. 4. Accuracy is taken as granted to measure the performance of the model. However, we argue on the relevance of this metric and support other metrics in conjunction with it. We engineer our model to incline towards the metrics that are found relevant for a given problem based on the classifier learning. 5. Feature quantification and function building governed by algorithms the way we presented is not found in the literature, and the dynamic ability of such a design, as our work indicated, can be a good filler of this gap in the state of the art. 6. Finally, FO has not been directly addressed. FO helps to regulate the error biasing, outlier detection, and poor generalization.