Switching Neural Networks (SNN)
- was developed in the 1990s to overcome the drawbacks of the most commonly used machine learning methods:
- in particular, black box methods, such as multilayer perceptron and support vector machine, had good accuracy but could not provide deep insight into the studied phenomenon
- on the other hand, decision trees were able to describe the phenomenon but often lacked accuracy
- Switching Neural Networks made use of Boolean algebra to build sets of intelligible rules able to obtain very good performance
- in 2014, an efficient version of Switching Neural Network was developed and implemented in the Rulex suite with the name Logic Learning Machine. Also, an LLM version devoted to regression problems was developed