Improved random forest classification approach based on hybrid clustering selection

Abstract

The random forest algorithm is an ensemble learning method, with the decision tree as its base classifier. In the ensemble model, it is not always true that the more the base classifiers, the better the classification effect, since if there are more base classifiers with poor performance in the model, they may have negative impacts on the final classification result. In order to modify the random forest classification method under the premise of ensuring the diversity of the random forest model, based on the random forest algorithm of cluster integration selection and personal indoor thermal preference model, this paper proposes a random forest method of clustering ensemble selection with Dunn index. Considering the shortcomings of the irrevocable merging strategy of hierarchical clustering algorithm, a random forest method of hybrid clustering ensemble selection based on hierarchical clustering and k- medoids partition clustering is developed. The effectiveness of the proposed methods is verified by classifying personal indoor thermal preferences.