Complexity Approximation of Classification Task for Large Dataset Ensemble Artificial Neural Networks

Mohamad, Mohd Saberi; Saman, Yazid Mohd; Hamid, Nazirah Abd

doi:10.1007/978-981-13-1799-6_21

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…A key concept of the ensemble technique is that it seeks to combine ideas from many individual classifiers in order to get superior results that complement one another [21]. The majority of prior research agrees that accuracy increases when employing an ensemble approach rather than a single classifier, with the condition that the mixers in the combinations must be accurate and varied in order for the accuracy to improve [17], [22].…”

Section: E Ensemble Methodsmentioning

confidence: 99%

Improving Customer Churn Classification with Ensemble Stacking Method

Awang¹,

Makhtar²,

Udin³

et al. 2021

IJACSA

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Due to the high cost of acquiring new customers, accurate customer churn classification is critical in any company. The telecommunications industry has employed single classifiers to classify customer churn; however, the classification accuracy remains low. Nevertheless, combining several classifiers' decisions improves classification accuracy. This article attempts to enhance ensemble integration via stack generalisation. This paper proposed a stacking ensemble based on six different learning algorithms as the base-classifiers and tested on five different meta-model classifiers. We compared the performance of the proposed stacking ensemble model with single classifiers, bagging and boosting ensemble. The performances of the models were evaluated with accuracy, precision, recall and ROC criteria. The findings of the experiments demonstrated that the proposed stacking ensemble model resulted in the improvement of the customer churn classification. Based on the results of the experiments, it indicates that the prediction accuracy, precision, recall and ROC of the proposed stacking ensemble with MLP meta-model outperformed other single classifiers and ensemble methods for the customer churn dataset.

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Section: E Ensemble Methodsmentioning

confidence: 99%

Improving Customer Churn Classification with Ensemble Stacking Method

Awang¹,

Makhtar²,

Udin³

et al. 2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…Providing highquality care at affordable rates is a significant problem for the healthcare industry, and accurate diagnoses are the hallmarks of high-quality care [3]. Data mining is an interdisciplinary subject of computer science and statistic whose overall objective is to extract information from a dataset and transform the data into a usable structure [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison between Meta-classifier Algorithms for Heart Disease Classification

Zaini¹,

Awang²

2022

IJACSA

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The rise in heart disease among the general population is alarming. This is because cardiovascular disease is the leading cause of death, and several studies have been conducted to assist cardiologists in identifying the primary cause of heart disease. The classification accuracy of single classifiers utilised in most recent studies to predict heart disease is quite low. The accuracy of classification can be enhanced by integrating the output of multiple classifiers in an ensemble technique. Even though they can deliver the best classification accuracy, the existing ensemble approaches that integrate all classifiers are quite resource-intensive. This study thus proposes a stacking ensemble that selects the optimal subset of classifiers to produce meta-classifiers. In addition, the research compares the effectiveness of several meta-classifiers to further enhance classification. There are ten types of algorithms, including logistic regression (LR), support vector classifier (SVC), random forest (RF), extra tree classifier (ETC), naïve bayes (NB), extreme gradient boosting (XGB), decision tree (DT), k-nearest neighbor (KNN), multilayer perceptron (MLP), and stochastic gradient descent (SGD) are used as a base classifier. The construction of the meta-classifier utilised three different algorithms consisting of LR, MLP, and SVC. The prediction results from the base classifier are then used as input for the stacking ensemble. The study demonstrates that using a stacking ensemble performs better than any other single algorithm in the base classifier. The meta-classifier of logistic regression yielded 90.16% results which is better than any base classifiers. In conclusion, we could assume that the ensemble stacking approach can be considered an additional means of achieving better accuracy and has improved the performance of the classification.

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Complexity Approximation of Classification Task for Large Dataset Ensemble Artificial Neural Networks

Cited by 2 publications

References 11 publications

Improving Customer Churn Classification with Ensemble Stacking Method

Improving Customer Churn Classification with Ensemble Stacking Method

Performance Comparison between Meta-classifier Algorithms for Heart Disease Classification

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