A Spark-based Ant Lion Algorithm for Parameters Optimization of Random Forest in Credit Classification

2019 ◽  
Author(s):  
Hongwei Chen ◽  
Pengyang Chang ◽  
Zhou Hu ◽  
Heng Fu ◽  
Lingyu Yan
Keyword(s):  
Random Forest ◽  
Parameters Optimization ◽  
Lion Algorithm ◽  
Ant Lion

A comparative analysis of the queuing search algorithm, the sine-cosine algorithm, the ant lion algorithm to determine the optimal weight design problem of a spur gear drive system

2021 ◽  
Vol 63 (5) ◽  
pp. 442-447
Author(s):  
Hammoudi Abderazek ◽  
Ferhat Hamza ◽  
Ali Riza Yildiz ◽  
Liang Gao ◽  
Sadiq M. Sait
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Spur Gear ◽  
Ant Lion Optimization ◽  
Lion Algorithm ◽  
Weight Design ◽  
Sine Cosine Algorithm ◽  
Ant Lion ◽  
Teaching Learning ◽  
Lion Optimization Algorithm

Abstract Metaheuristic optimization algorithms have gained relevance and have effectively been investigated for solving complex real design problems in diverse fields of science and engineering. In this paper, a recent meta-heuristic approach inspired by human social concepts, namely the queuing search algorithm (QSA), is implemented for the first time to optimize the main parameters of the spur gear, in particular, to minimize the weight of a single-stage spur gear. The effectiveness of the algorithm introduced is examined in two steps. First, the algorithm used is compared with descriptions in previous studies and indicates that the final results obtained by QSA lead to a reduction in gear weight by 7.5 %. Furthermore, the outcomes obtained are compared with those for the other five algorithms. The results reveal that the QSA outperforms the techniques with which it is compared such as the sine-cosine optimization algorithm, the ant lion optimization algorithm, the interior search algorithm, the teaching-learning-based algorithm, and the jaya algorithm in terms of robustness, success rate, and convergence capability.

scholarly journals Optainet-based technique for SVR feature selection and parameters optimization for software cost prediction

2021 ◽  
Vol 348 ◽  
pp. 01002
Author(s):  
Assia Najm ◽  
Abdelali Zakrani ◽  
Abdelaziz Marzak
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Prediction Models ◽  
Project Managers ◽  
Parameters Optimization ◽  
Support Vector ◽  
Features Selection ◽  
Selection Methods ◽  
Cost Prediction ◽  
Software Cost

The software cost prediction is a crucial element for a project’s success because it helps the project managers to efficiently estimate the needed effort for any project. There exist in literature many machine learning methods like decision trees, artificial neural networks (ANN), and support vector regressors (SVR), etc. However, many studies confirm that accurate estimations greatly depend on hyperparameters optimization, and on the proper input feature selection that impacts highly the accuracy of software cost prediction models (SCPM). In this paper, we propose an enhanced model using SVR and the Optainet algorithm. The Optainet is used at the same time for 1-selecting the best set of features and 2-for tuning the parameters of the SVR model. The experimental evaluation was conducted using a 30% holdout over seven datasets. The performance of the suggested model is then compared to the tuned SVR model using Optainet without feature selection. The results were also compared to the Boruta and random forest features selection methods. The experiments show that for overall datasets, the Optainet-based method improves significantly the accuracy of the SVR model and it outperforms the random forest and Boruta feature selection methods.

scholarly journals Prediction of O-glycosylation Sites Using Random Forest and GA-Tuned PSO Technique

2015 ◽  
Vol 9 ◽  
pp. BBI.S26864 ◽  
Author(s):  
Hebatallah Hassan ◽  
Amr Badr ◽  
M. B. Abdelhalim
Keyword(s):  
Genetic Algorithm ◽  
Random Forest ◽  
Classification Accuracy ◽  
Characteristic Curve ◽  
Pso Algorithm ◽  
Glycosylation Site ◽  
Protein Glycosylation ◽  
Parameters Optimization ◽  
Minority Class ◽  
Glycosylation Sites

O-glycosylation is one of the main types of the mammalian protein glycosylation; it occurs on the particular site of serine (S) or threonine (T). Several O-glycosylation site predictors have been developed. However, a need to get even better prediction tools remains. One challenge in training the classifiers is that the available datasets are highly imbalanced, which makes the classification accuracy for the minority class to become unsatisfactory. In our previous work, we have proposed a new classification approach, which is based on particle swarm optimization (PSO) and random forest (RF); this approach has considered the imbalanced dataset problem. The PSO parameters setting in the training process impacts the classification accuracy. Thus, in this paper, we perform parameters optimization for the PSO algorithm, based on genetic algorithm, in order to increase the classification accuracy. Our proposed genetic algorithm-based approach has shown better performance in terms of area under the receiver operating characteristic curve against existing predictors. In addition, we implemented a glycosylation predictor tool based on that approach, and we demonstrated that this tool could successfully identify candidate glycosylation sites in case study protein.

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