In a previous work (Sanchez et al. 2013), we presented a heuristic search algorithm called CSA (Configuration Selection Algorithm 1 for solving the optimization problem resulting from selecting a valid configuration of a system based on feature models.
The construction of models based on feature reduction such as principal component analysis (PCA) [22] and partial least squares regression (PLS) [23] compress the original dataset generating a smaller number of variables.
CAD models are based on feature constraints to create and define an assembly.
These models are based on feature engineering and deep learning, and have already achieved state-of-the-art accuracies in English sentiment analysis.
Based on feature rankings derived from linear SVR models and an averaged z-score threshold of 1.5, 61 mutations were selected as significant features for each of the two RC datasets independently.
A tool, based on that feature model, is presented to model the mobile application features and to derive the SPL members.
Simulation of the performance of refracturing and calibration of the CRV are two applications of FFM on fracture modeling based on features of the fractal fracture.
Shihab et al. (2010 20122) developed a decision tree model, based on features about the bug report, the bug fix, and human factors, to predict which bugs would be reopened.
Li et al. [7] propose a multi-target (i.e., moving human/vehicle) tracking algorithm using a KF motion model, based on features including the center of mass and tracking window of moving targets.
Most of the existing computational approaches predict the pathogenic effect using statistical methods, machine learning techniques, or protein evolution models, based on features such as sequence homology, biochemical properties, and structural information (hydrogen-bond network, solvent accessibility, and so on).
