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

Rojarath, Artittayapron; Songpan, Wararat

doi:10.1007/s10489-020-02106-3

Cited by 12 publications

(8 citation statements)

References 79 publications

(112 reference statements)

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“…achieved through different methods. Rojarath and Songpan [119] addressed the issue of multi-class data. They proposed a cost-sensitivity matrix of the true positive (TP).…”

Section: Related Workmentioning

confidence: 99%

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Ensemble learning based on relative accuracy approach and diversity teams

B. Rokaya,

D. Alsufiani

2024

Bulletin EEI

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Ensemble learning, which involves combining the opinions of multiple experts to arrive at a better result, has been used for centuries. In this work, a review of the major voting methods in ensemble learning is explored. This work will focus on a new method for combining the results of individual learners. The method depends on the relative accuracy and diversity of teams. Instead of trying to assign weight to each different trainer, the concept of diversity teams is presented. Each team will vote as one player; however, the individual accuracies of each learner still be implemented. The concept of relaxing parameters that deal with each team as one player is presented. Our experiments demonstrate that traditional ensemble voting methods outperform individual learners. There is a limit to the superiority of the ensemble learner that any ensemble learner cannot go beyond. The proposed voting method gives the same results as the traditional ensemble voting methods, however, a different diversity of the proposed method from the traditional voting method or for different values of the relaxing parameter can be achieved.

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“…achieved through different methods. Rojarath and Songpan [119] addressed the issue of multi-class data. They proposed a cost-sensitivity matrix of the true positive (TP).…”

Section: Related Workmentioning

confidence: 99%

“…Kim et al [113] proposed a two-stage weighting method for voting in ensemble learning. Some works proposed different methods for voting in classification problems [114]- [119]. In the related works section details of voting works will be discussed.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning based on relative accuracy approach and diversity teams

B. Rokaya,

D. Alsufiani

2024

Bulletin EEI

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“…The weights were obtained by the correct prediction rate of the classes from the true positive values. The basic classifier techniques [38] are combined as shown in equations (6) ( 7) and (8).…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

Weighted Voting Ensemble for Depressive Disorder Analysis with Multi-objective Optimization

Nuankaew

Doenribram

et al. 2022

Curr. Appl. Sci. Technol.

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The Twitter platform is a popular tool that is widely used by researchers to collect data on users’ personal lives, feelings and emotions. These data sets can be further analyzed using text mining techniques to predict the disorder of depression. There are nine symptoms of depression that are classified by American Psychiatric Association using DSM-5 criteria. The symptoms can be difficult to identify effectively. The unweighted vote ensemble is not practical for multi-class data. Therefore, this research proposes the multi-objective optimization algorithms for depressive symptom prediction modeling (MOADSP) for the weighted voting ensemble, which can improve its effectiveness compared to the singer model. The objectives of this research were 1) to find the appropriate number of features; 2) to improve the weights of the prediction models based on the recall of the class for the ensemble; and 3) to compare the performance of the single, unweighted, and weighted voting ensemble models for depressive disorder. An information gain was used to select the features. The single classification techniques used in the experiment that had their frameworks tested were the Naïve Bayes, Random Forest, and K-Nearest techniques, while the vote ensemble models used were the unweighted and weighted models. MOADSP was applied to the weighted vote ensemble models. The results showed that the best recall classifier was KNN (98.60%), and the highest recall classifier was AVG TP weighted (98.43%) for the training model. The highest recall in the class depressive classifier was AVG TP weighted (80.00%) for the testing. This proposed method was beneficial for the prediction of depressive disorder.

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“…Despite the numerous proposed weight assignment methods, finding the suitable weight configuration remains a challenging task. At present, the most common practice is to assign weights according to the prediction accuracy of the predictor [ 36 , 37 ]. However, when the prediction accuracy gap between the predictors is too large, this method cannot guarantee the integrated results better than the results of a single predictor.…”

Section: Introductionmentioning

confidence: 99%

Combination prediction method of students’ performance based on ant colony algorithm

Xu,

Kim

2024

PLoS ONE

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Students’ performance is an important factor for the evaluation of teaching quality in colleges. The prediction and analysis of students’ performance can guide students’ learning in time. Aiming at the low accuracy problem of single model in students’ performance prediction, a combination prediction method is put forward based on ant colony algorithm. First, considering the characteristics of students’ learning behavior and the characteristics of the models, decision tree (DT), support vector regression (SVR) and BP neural network (BP) are selected to establish three prediction models. Then, an ant colony algorithm (ACO) is proposed to calculate the weight of each model of the combination prediction model. The combination prediction method was compared with the single Machine learning (ML) models and other methods in terms of accuracy and running time. The combination prediction model with mean square error (MSE) of 0.0089 has higher performance than DT with MSE of 0.0326, SVR with MSE of 0.0229 and BP with MSE of 0.0148. To investigate the efficacy of the combination prediction model, other prediction models are used for a comparative study. The combination prediction model with MSE of 0.0089 has higher performance than GS-XGBoost with MSE of 0.0131, PSO-SVR with MSE of 0.0117 and IDA-SVR with MSE of 0.0092. Meanwhile, the running speed of the combination prediction model is also faster than the above three methods.

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

Cited by 12 publications

References 79 publications

Ensemble learning based on relative accuracy approach and diversity teams

Ensemble learning based on relative accuracy approach and diversity teams

Weighted Voting Ensemble for Depressive Disorder Analysis with Multi-objective Optimization

Combination prediction method of students’ performance based on ant colony algorithm

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