Heterogeneous ensemble classifiers for Malay syllables classification

2020 ◽  
Author(s):  
Zaridah Mat Zain ◽  
Zulkhairi Mohd Yusuf ◽  
Hariharan Muthusamy ◽  
Kushsairy Abd Kader ◽  
Nurul Aida Mohd Mortar
Keyword(s):  
Ensemble Classifiers ◽  
Heterogeneous Ensemble

Hardware acceleration of homogeneous and heterogeneous ensemble classifiers

2015 ◽  
Vol 39 (8) ◽  
pp. 782-795 ◽  
Author(s):  
Vuk S. Vranjković ◽  
Rastislav J.R. Struharik ◽  
Ladislav A. Novak
Keyword(s):  
Hardware Acceleration ◽  
Ensemble Classifiers ◽  
Heterogeneous Ensemble

scholarly journals Comparing Performance of Machine Learning Algorithms for Default Risk Prediction in Peer to Peer Lending

TEM Journal ◽  
2021 ◽  
pp. 133-143
Author(s):  
Yanka Aleksandrova
Keyword(s):  
Machine Learning ◽  
Default Risk ◽  
Credit Scoring ◽  
Learning Algorithms ◽  
Peer To Peer ◽  
Machine Learning Algorithms ◽  
Ensemble Classifiers ◽  
Peer Lending ◽  
Official Site ◽  
Heterogeneous Ensemble

The purpose of this research is to evaluate several popular machine learning algorithms for credit scoring for peer to peer lending. The dataset to fit the models is extracted from the official site of Lending Club. Several models have been implemented, including single classifiers (logistic regression, decision tree, multilayer perceptron), homogeneous ensembles (XGBoost, GBM, Random Forest) and heterogeneous ensemble classifiers like Stacked Ensembles. Results show that ensemble classifiers outperform single ones with Stacked Ensemble and XGBoost being the leaders.

scholarly journals Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns

2020 ◽  
Vol 10 (5) ◽  
pp. 1745 ◽  
Author(s):  
Hamad Alsawalqah ◽  
Neveen Hijazi ◽  
Mohammed Eshtay ◽  
Hossam Faris ◽  
Ahmed Al Radaideh ◽  
...  
Keyword(s):  
Main Idea ◽  
Ensemble Classification ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Ensemble Classifiers ◽  
Software Developers ◽  
Software Defect ◽  
Software Modules ◽  
Benchmark Datasets ◽  
Heterogeneous Ensemble

Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its own strengths and weaknesses. The main idea of the proposed approach is to develop expert and robust heterogeneous classification models. Two versions of the proposed approach are developed and experimented. The first is based on simple classifiers, and the second is based on ensemble ones. For evaluation, 21 publicly available benchmark datasets are selected to conduct the experiments and benchmark the proposed approach. The evaluation results show the superiority of the ensemble version over other well-regarded basic and ensemble classifiers.

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