Table 1 Comparisons between machine learning algorithms

Deep learning

CNN

High accuracy; Weight-sharing; Relieves the model overfitting problem; Shift-invariant feature enhances the robustness of the network

The pooling layer loses a lot of valuable information; Substantial hardware and dataset size requirements [41]; No memory function; Shift-invariant feature also prevents the neuron that recognizes the object from being activated when the object changes slightly

Computer vision [40, 41]; Natural language processing [40]

Elman RNN

Strong ability to extract time series features; Better generalization ability

Prone to show gradient exploding and gradient vanishing; Unable to solve the problem of long-term dependencies and parallel training [42]

Time series data, e.g., natural language processing [22]

LMST

Achieve better analysis results in longer sequences; Solving vanishing gradient problem and stability problems in the time dimension of Elman RNN

When processing longer sequences data, LSTM is still difficult; Time-consuming [22]

For processing longer time series data than Elman RNN, such as in natural language processing [22]

GAN

Generative model; Can still be used when the probability density is not calculated; Good at generalization

Unstable training process; Difficult to achieve Nash equilibrium; Not suitable for processing discrete forms of data

Data augmentation [46]; Text-to-image synthesis [26]; Image-to-image translation [26]; Computer vision [25]

Traditional machine learning

MLP

Simple model; Easy implementation, and good generalization ability

architecture optimization; For training large datasets is very time-consuming [27]

Identification; Classification and prediction [32]

SVM

Suitable for small-sample binary classification research; Good robustness and generalization ability

Sensitive to parameters and kernel function; Inappropriate for multi-classification research in non-optimized cases

Classification and Regression problems [32]

DT

Simple to understand and to interpret; Requires little data preparation

Unstable for small variations in the data might result in the generation of a completely different tree

classification, and regression problems [32]

RF

Simple structure; Easy to implement; Higher efficiency [37]

Unable to optimize its own parameters; Overfitting can easily occur when the amount of data is large [45]

Classification and regression problems [44]