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Table 3 Comparison of strengths and weaknesses of machine learning algorithms in electronic nose studies

From: Diagnosis of ventilator-associated pneumonia using electronic nose sensor array signals: solutions to improve the application of machine learning in respiratory research

  Strengths Weaknesses
k-nearest neighbors • Make no assumption about underlying data distribution • Does not produce a model, limiting the ability to understand how the features are related to the class
 • If there are more samples of one class than other class, the dominant class will control the classification and cause wrong classification
Naive Bayes • Requires relatively few examples for training • Relies on an often-faulty assumption of equally important and independent features
 • Not ideal for datasets with many numeric features
Decision tree • Can be used on small dataset • It is easy to overfit or underfit the model
 • Model is easy to interpret • Small changes in the training data can result in large changes to decision logic
Neural network • Conceptually similar to human neural function • Very prone to overfitting training data
 • Capable of modeling more complex patterns • Susceptible to multicollinearity
Support vector machines • High accuracy but not overly influenced by noisy data and not very prone to overfitting • Finding the best model requires testing of various combinations of kernels and model parameters
 • Easier for users due to the existence of several well-supported SVM algorithms
 • Most commonly used
Random forest • Can handle noisy or missing data • The model is not easily interpretable
 • Suitable for class imbalance problems
  1. Summarized from [27, 53, 54]