1. Determine the best attributes: the number of attributes greatly affect the performance of ANFIS.
Many research studies have touched on this issue; some of them aggregate the attributes into a small number of attributes, which reduces the computational time but reduces the accuracy of prediction dramatically as well. Another group of research studies determined the best attribute heuristically.
The information gain and Gini index can be used to select the best attributes to build the model using a small number of attributes, also the human expert can help to select the suitable attributes. A lot of techniques can be studied in the future to reduce the number of inputs by selecting the best attributes.
2. Sampling: sampling is a statistical process in which a number of instances are taken from large population. The sampling might be random sampling or systematic sampling. As the size of the dataset and the number of rules has an effect on the efficiency of the ANFIS algorithm, the sampling can be used to reduce the size of the rule by taking a stratified random set which may give good accuracy and reduce the computational time.
3. Reduce the computational time: time is required to build the model using ANFIS, especially when the number of attributes is large, or is long. A lot of research studies are performed to reduce the running time to produce the model, some of which focus on the membership distribution function by selecting the simple function or by reducing the modification process, and other research studies focus on pruning the insignificant rules to reduce the number of rules that will be processed, then reducing the time. Time reduction is a very important topic that needs a lot of research studies to solve it.
4. Study different features: modification of the computed consequent and the membership distribution function, carried out by LSE and gradient descent, respectively. A lot of research studies are performed to modify the adaptable node in ANFIS, such as using Genetic algorithm and Bee Colony, but this topic needs further research to enhance the modification process of the adaptable node in ANFIS.
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