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| Classifier | Advantages | Limitations |
|
| Support vector machine (SVM) | (1) Suitable for small and clean datasets | (1) Less efficient on datasets that have noise |
| (2) Effective in high dimensional spaces | (2) Unsuitable for big datasets |
| (3) Hard to choose a suitable kernel-function that is robust to interpret |
|
| Dynamic time warping (DTW) | (1) It is time series averaging which makes the classification faster more accurate | (1) The number of templates is restricted |
| (2) Suitable for a smaller number of templates | (2) Actual training samples is required |
|
| Deep learning | (1) Computation power does not affect it | (1) Hard to understand |
| (2) High dimensional | (2) For training, it require large quantity of data for training |
| (3) Can automatically adapt all data | (3) Large memory and computing resources is required |
| (4) Faster in obtaining results | (4) More costly |
| (5) Works on big and complex datasets | (5) High errors rate |
|
| K-nearest neighbor (K-NN) | (1) The complete dataset is covered for finding K-nearest neighbors | (1) Sensitive to outliers |
| (2) Cannot handle the missing value issue |
| (2) Suitable for multi-class classification and regression problems | (3) Mathematically costly |
| (4) Large memory is required |
| (5) Homogeneous features is required |
|
| Probabilistic neural network (PNN) | (1) Quicker and more accurate than MLPs | (1) More memory space is needed |
| (2) Insensitive to outliers | (2) When it compared to MLP it is slower in case of new classification samples |
| (3) Representative training set is required |
|
| Euclidean distance | (1) Very popular method | Sensitive to outliers |
| (2) Easy computation |
| (3) Works good with compact or isolated clusters |
| Manhattan distance | Dealing good with datasets with compact or isolated clusters | Sensitive to the outliers |
| Hidden markov model (HMM) | Can handle inputs with variable length | More memory and time it requirement |
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