Intelligent optimizing
algorithms (IOA)-based | GA(Genetic algorithm) | Holand, 1975[43] | Return to the mathematical essence of variable combination optimization, retain advantages of the combination of variables; Too many combinations of variables to optimize, usually need more preset parameters, sometimes easy to fall into local optimum. |
| SA(Simulated Annealing) | Metropolis, 1953[44] |
| PSO(Particle swarm optimization) | Eberhart&Kennedy, 1995[45] |
| ACO(Ant colony optimization) | Colorni, 1991[46] |
| GWO(Gray wolf optimizer) | Mirjalili, 2014[47] |
Model population
analysis (MPA)-based | BOSS (Bootstrapping soft shrinkage) | Liang, 2016[48] | The traditional strategy of rigidly eliminating variables according to a single index is transformed into a flexible strategy of changing weight, which can preserve the effective variables more safely; The introduction of random algorithm helps to preserve the combination effect among spectral variables, however, it also makes the calculation more complicated. |
VCPA (Variable combination
population analysis) | Liang, 2015[49] |
VISSA (Variable iterative space
shrinkage approach) | Liang, 2014[50] |
| ICO (Interval combination optimization) | Xiong & Min, 2016[51] |
| iRF (internal Random frog) | Liang, 2013[52] |
Collinearity
minimization-based | SPA (Successive projection
algorithm)[53, 54] | Araujo, 2001[55] | Minimizing the influence of multi-collinearity variables on the model; In the optimization, each variable is used as the starting point, the calculation amount is too large to be suitable for small-size sample. |
SR (Stepwise
regression)[56] | |
| Category model-based | LDA (Linear discriminant analysis) | Fisher, 1936[57] | The correlation between variables and model is preserved, and the overall prediction accuracy is improved by combining different classification algorithms. The computational complexity is small, but the result is limited by the performance of the classification model. |
ULDA (Uncorrelated
lineardiscriminant analysis)[58] | Jin, 2001[59] |
| RF (Random forest)[60,61,62] | Breiman, 2001[63] |
| SVM (Support vector machine) | Vapnik, 1995[64] |
| Regularization method | LASSO (Least absolute shrinkage
and selection operator)[65] | Tibshirani, 1996[66] | Parameter estimation and variable selection are realized simultaneously, fast. When the number of variables is large, the over-fitting can be avoided; The suitable parameter value should be chosen. |
| EN (Elastic net) | Zou, 2003[67] |
| RR (Ridge regression) | Hoerl & Kennard, 1998[68] |
