Dataset Feasibility Analysis Method based on Enhanced Adaptive LMS method with Min-max Normalization and Fuzzy Intuitive Sets

A good dataset was required for attaining good accuracy in machine learning, especially in prediction, so that prediction accuracy was high. The imbalanced or too small dataset was a common problem in machine learning. This study proposed a method for determining the dataset's quality. If the dataset is not feasible, preprocessing can be performed to improve the dataset's quality before making predictions. Adaptive Least Mean Square (LMS) was merged with Min-max Normalization and Fuzzy Intuitive Sets (FIS) algorithms to create the proposed technique. This method might assess the value of uncertainty and information, which will influence the dataset's feasibility. If the dataset has an uncertainty value closed 1.5 and an information value of less than 0.5, it is usable. The method has been tested on both public and private datasets. According to all experiments conducted, the uncertainty value and information value on the stated threshold can have prediction accuracy above 70% with various methodologies.