Abstract

Applying approaches such as crowding distance between samples based on Non-Dominated Sorting Genetic Algorithm-II can gradually improve the feature subset by selecting the most relevant features based on their distance values. The number of subsets has been decreased for the applied dataset from (41) features to (29) features with accuracy maintained after applying machine learning techniques. K-NN, SVM, and DT provided an improvement in predicted accuracy from (80.81, 76.6, and 86.7) to (81.9, 81.6, and 87.6) with features minimizations based on the proposed model. In addition, a study was done on the applied dataset for level and basic types of IoT regarding its affection on prediction accuracy with specifying a value of these features.