The improved ensemble classification technique using Wolf algorithm

Abstract

The rise of data mining leads to the data-oriented society and focusing on data analytics. Several classification techniques have been investigated to find the optimized model for data prediction. This includes the enhancing the performance of the model. Grey Wolf Optimizer is the one of novel approach to solve NP-hard problems. However, the algorithm address the general situation. To solve the customized situation, the adapted algorithm needs to be explored. This research proposes the Ensemble Featured-Wolf (EF-Wolf) algorithm which includes the feature selection stage and implements ensemble technique to optimize the function selection problem in classification. The number of the packs of the wolf can help to select the most optimized functions to selection the most relevant features in the dataset. In addition, the packs ensemble of the relevant features can determine the feature selection of the dataset. The experiment shows the comparison among classification techniques with binary and multiclass datasets. The results show that EF-Wolf 5-pack mostly performs better results in terms of accuracy rate compared to other techniques.