Cost-sensitive probability for weighted voting in an ensemble model for multi-class classification problems

Ensemble learning is an algorithm that utilizes various types of classification models. This algorithm can enhance the prediction efficiency of component models. However, the efficiency of combining models typically depends on the diversity and accuracy of the predicted results of ensemble models. However, the problem of multi-class data is still encountered. In the proposed approach, cost-sensitive learning was implemented to evaluate the prediction accuracy for each class, which was used to construct a cost-sensitivity matrix of the true positive (TP) rate. This TP rate can be used as a weight value and combined with a probability value to drive ensemble learning for a specified class. We proposed an ensemble model, which was a type of heterogenous model, namely, a combination of various individual classification models (support vector machine, Bayes, K-nearest neighbour, naive Bayes, decision tree, and multi-layer perceptron) in experiments on 3-, 4-, 5- and 6-classifier models. The efficiencies of the propose models were compared to those of the individual classifier model and homogenous models (Adaboost, bagging, stacking, voting, random forest, and random subspaces) with various multi-class data sets. The experimental results demonstrate that the cost-sensitive probability for the weighted voting ensemble model that was derived from 3 models provided the most accurate results for the dataset in multi-class prediction. The objective of this study was to increase the efficiency of predicting classification results in multi-class classification tasks and to improve the classification results.

Automated summary

Ensemble learning combines diverse classification models to improve prediction efficiency, utilizing cost-sensitive learning to enhance accuracy. Experimental results show that the weighted voting ensemble model derived from three models provides the most accurate results in multi-class prediction.