Journal articles: 'Genetic algorithm basedselected ensemble'

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Min, Sung-Hwan. "Genetic Algorithm based Hybrid Ensemble Model." Journal of Information Technology Applications and Management 23, no. 1 (2016): 45–59. http://dx.doi.org/10.21219/jitam.2016.23.1.045.

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Lee, Seogyoung, Martin Seunghwan Yang, Jongkyeong Kang, and Seung Jun Shin. "Ensemble variable selection using genetic algorithm." Communications for Statistical Applications and Methods 29, no. 6 (2022): 629–40. http://dx.doi.org/10.29220/csam.2022.29.6.629.

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Watanabe, Ryuya, and Lei Li. "Generation of Sparse AutoEncoder and Ensemble Learning Based on the Genetic Algorithm." Information 27, no. 2 (2024): 111–29. http://dx.doi.org/10.47880/inf2702-02.

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In this paper, we discuss optimization of parameters for generation of sparse AutoEncoder based on the genetic algorithm, and show an efficient ensemble learning algorithm. From experiment result of some data sets, we generated better sparse AutoEncoder, and get ensemble effect from the diversity of data in the middle layer of AutoEncoder. Keywords: Sparse AutoEncoder, Ensemble Learning, Genetic Algorithm

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Kim, Young-Won, and Il-Seok Oh. "Hybrid Genetic Algorithm for Classifier Ensemble Selection." KIPS Transactions:PartB 14B, no. 5 (2007): 369–76. http://dx.doi.org/10.3745/kipstb.2007.14-b.5.369.

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Reza Ghaemi. "Pattern Ensemble Learning Method for Clustering Ensemble using Incremental Genetic-Based Algorithm." Power System Technology 49, no. 1 (2025): 24–52. https://doi.org/10.52783/pst.1389.

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The clustering ensemble has emerged as a prominent method for improving clustering accuracy of unsupervised classification. It combines multiple partitions generated by different clustering algorithms into a single clustering solution. Genetic algorithms are known as methods with high ability to solve optimization problems including clustering. To date, significant progress has been contributed to find consensus clustering that will yield better results than existing clustering. This paper has proposed an Incremental Genetic-Based Algorithm for Clustering Ensemble (IGCE) to perform the search task, but has replaced its traditional crossover operator with a Pattern Ensemble Learning Method (PEL). Therefore, IGCE-PEL is capable to avoid the problems of clustering invalidity and context insensitivity from the traditional crossover operator of genetic algorithms. IGCEs have been evaluated on twelve benchmark datasets based on different recombination operators used. The experimental results have demonstrated that IGCE using PEL is able to achieve better clustering accuracy when compared with several other existing genetic-based clustering ensemble algorithms.

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Wang, Yanhua, Xiyu Liu, and Laisheng Xiang. "GA-Based Membrane Evolutionary Algorithm for Ensemble Clustering." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4367342.

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Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. Genetic algorithm is a highly parallel, stochastic, and adaptive search algorithm developed from the natural selection and evolutionary mechanism of biology. In this paper, an improved genetic algorithm is designed by improving the coding of chromosome. A new membrane evolutionary algorithm is constructed by using genetic mechanisms as evolution rules and combines with the communication mechanism of cell-like P system. The proposed algorithm is used to optimize the base clusterings and find the optimal chromosome as the final ensemble clustering result. The global optimization ability of the genetic algorithm and the rapid convergence of the membrane system make membrane evolutionary algorithm perform better than several state-of-the-art techniques on six real-world UCI data sets.

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Das, Asit K., Sunanda Das, and Arka Ghosh. "Ensemble feature selection using bi-objective genetic algorithm." Knowledge-Based Systems 123 (May 2017): 116–27. http://dx.doi.org/10.1016/j.knosys.2017.02.013.

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Chatterjee, Sujoy, and Anirban Mukhopadhyay. "Clustering Ensemble: A Multiobjective Genetic Algorithm based Approach." Procedia Technology 10 (2013): 443–49. http://dx.doi.org/10.1016/j.protcy.2013.12.381.

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Oh, Dong-Yop, and J. Brian Gray. "GA-Ensemble: a genetic algorithm for robust ensembles." Computational Statistics 28, no. 5 (2013): 2333–47. http://dx.doi.org/10.1007/s00180-013-0409-6.

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KIM, YOUNG-WON, and IL-SEOK OH. "COARSE-TO-FINE CLASSIFIER ENSEMBLE SELECTION USING CLUSTERING AND GENETIC ALGORITHMS." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 06 (2009): 1083–106. http://dx.doi.org/10.1142/s021800140900751x.

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A good classifier ensemble should show high complementarity among classifiers to produce a high recognition rate and it should also have a small size to be efficient. This paper proposes a classifier ensemble selection algorithm operating in a coarse-to-fine paradigm. For the algorithm to be successful, the original classifier pool should be sufficiently diverse. So this paper produces a large classifier pool by combining several different classification algorithms and several feature subsets. The coarse selection stage reduces greatly the size of the classifier pool using a clustering algorithm. The fine selection finds the near-optimal ensemble using genetic algorithms. A hybrid genetic algorithm with improved searching capability is also proposed. The experimentation used handwritten numeral datasets and UCI datasets. The experimental results and the test of statistical significance showed that the proposed algorithm is superior to the conventional ones.

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Merta, Jan, and Tomáš Brandejský. "Two-layer genetic programming." Neural Network World 32, no. 4 (2022): 215–31. http://dx.doi.org/10.14311/nnw.2022.32.013.

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This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.

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Merta, Jan, and Tomáš Brandejský. "Two-layer genetic programming." Neural Network World 32, no. 4 (2022): 215–31. http://dx.doi.org/10.14311/nnw.2022.27.013.

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This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.

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Liu, Kun-Hong, Muchenxuan Tong, Shu-Tong Xie, and Vincent To Yee Ng. "Genetic Programming Based Ensemble System for Microarray Data Classification." Computational and Mathematical Methods in Medicine 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/193406.

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Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.

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Dash, Sujata, and Bichitrananda Patra. "Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble." International Journal of Knowledge Discovery in Bioinformatics 6, no. 1 (2016): 1–16. http://dx.doi.org/10.4018/ijkdb.2016010101.

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High dimension and small sample size is an inherent problem of gene expression datasets which makes the analysis process more complex. The present study has developed a novel learning scheme that encapsulates a hybrid evolutionary fuzzy-rough feature selection model with an adaptive neural net ensemble. Fuzzy-rough method deals with uncertainty and impreciseness of real valued gene expression dataset and evolutionary search concept optimizes the subset selection process. The efficiency of the hybrid-FRGSNN model is evaluated by the proposed neural net ensemble learning algorithm. Again to prove the learning capability of ensemble algorithm, performance of the component classifiers pairing with FR, GSNN and FRGSNN are compared with proposed hybrid-FRGSNN based ensemble model. In addition to this, efficiency of neural net ensemble is compared with two classical and one advanced ensemble learning algorithms.

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Thammasiri, Dech, and Phayung Meesad. "Ensemble Data Classification based on Diversity of Classifiers Optimized by Genetic Algorithm." Advanced Materials Research 433-440 (January 2012): 6572–78. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6572.

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In this research we propose an ensemble classification technique base on creating classification from a variety of techniques such as decision trees, support vector machines, neural networks and then choosing optimize the appropriate classifiers by genetic algorithm and also combined by a majority vote in order to increase classification accuracy. From classification accuracy test on Australian Credit, German Credit and Bankruptcy Data, we found that the proposed ensemble classification models selected by genetic algorithm yields highest performance and our algorithms are effective in building ensemble.

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Houtekamer, P. L., Bin He, Dominik Jacques, et al. "Use of a Genetic Algorithm to Optimize a Numerical Weather Prediction System." Monthly Weather Review 149, no. 4 (2021): 1089–104. http://dx.doi.org/10.1175/mwr-d-20-0238.1.

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AbstractAn important step in an ensemble Kalman filter (EnKF) algorithm is the integration of an ensemble of short-range forecasts with a numerical weather prediction (NWP) model. A multiphysics approach is used in the Canadian global EnKF system. This paper explores whether the many integrations with different versions of the model physics can be used to obtain more accurate and more reliable probability distributions for the model parameters. Some model parameters have a continuous range of possible values. Other parameters are categorical and act as switches between different parameterizations. In an evolutionary algorithm, the member configurations that contribute most to the quality of the ensemble are duplicated, while adding a small perturbation, at the expense of configurations that perform poorly. The evolutionary algorithm is being used in the migration of the EnKF to a new version of the Canadian NWP model with upgraded physics. The quality of configurations is measured with both a deterministic and an ensemble score, using the observations assimilated in the EnKF system. When using the ensemble score in the evaluation, the algorithm is shown to be able to converge to non-Gaussian distributions. However, for several model parameters, there is not enough information to arrive at improved distributions. The optimized system features slight reductions in biases for radiance measurements that are sensitive to humidity. Modest improvements are also seen in medium-range ensemble forecasts.

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Feng, Xuanang, Jianing Zhao, and Eisuke Kita. "Genetic Algorithm-based Optimization of Deep Neural Network Ensemble." Review of Socionetwork Strategies 15, no. 1 (2021): 27–47. http://dx.doi.org/10.1007/s12626-021-00074-9.

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Huspi, Sharin Hazlin, and Chong Ke Ting. "Genetic Algorithm Ensemble Filter Methods on Kidney Disease Classification." International Journal of Innovative Computing 11, no. 2 (2021): 73–80. http://dx.doi.org/10.11113/ijic.v11n2.345.

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Kidney failure will give effect to the human body, and it can lead to a series of seriously illness and even causing death. Machine learning plays important role in disease classification with high accuracy and shorter processing time as compared to clinical lab test. There are 24 attributes in the Chronic K idney Disease (CKD) clinical dataset, which is considered as too much of attributes. To improve the performance of the classification, filter feature selection methods used to reduce the dimensions of the feature and then the ensemble algorithm is used to identify the union features that selected from each filter feature selection. The filter feature selection that implemented in this research are Information Gain (IG), Chi-Squares, ReliefF and Fisher Score. Genetic Algorithm (GA) is used to select the best subset from the ensemble result of the filter feature selection. In this research, Random Forest (RF), XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Naïve Bayes classification techniques were used to diagnose the CKD. The features subset that selected are different and specialised for each classifier. By implementing the proposed method irrelevant features through filter feature selection able to reduce the burden and computational cost for the genetic algorithm. Then, the genetic algorithm able to perform better and select the best subset that able to improve the performance of the classifier with less attributes. The proposed genetic algorithm union filter feature selections improve the performance of the classification algorithm. The accuracy of RF, XGBoost, KNN and SVM can achieve to 100% and NB can achieve to 99.17%. The proposed method successfully improves the performance of the classifier by using less features as compared to other previous work.

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Mohammadi, Mehdi, Ali Azadeh, Morteza Saberi, and Amir Azaron. "Genetic algorithm-based clustering ensemble: determination number of clusters." International Journal of Business Forecasting and Marketing Intelligence 1, no. 3/4 (2010): 201. http://dx.doi.org/10.1504/ijbfmi.2010.036004.

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Liu, Yang, Bo He, Diya Dong, et al. "Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/504120.

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A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, including batch learning and online learning. Second, an adaptive selective ensemble framework for online learning is designed to balance the accuracy and speed of the algorithm. Experiments for both regression and classification problems with UCI data sets are carried out. Comparisons between OS-ELM, simple ensemble OS-ELM (EOS-ELM), genetic algorithm based selective ensemble (GASEN) of OS-ELM, and the proposed particle swarm optimization based selective ensemble of OS-ELM empirically show that the proposed algorithm achieves good generalization performance and fast learning speed.

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Thirumalaiappan Ramanathan, Thirumalaimuthu, Md Jakir Hossen, and Md Shohel Sayeed. "Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease." Vol. 6 No. 1 (2024) 6, no. 1 (2024): 29–35. http://dx.doi.org/10.33093/ijoras.2024.6.1.5.

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Designing a hybrid or ensemble data mining system appropriate to the application is a research challenge. Heart disease is a life threatening disease that need to be recognized correctly in the starting stage before it becomes more complex. Using artificial intelligence techniques in a hybrid and ensemble architecture can support the prediction of heart disease more effectively based on the given sample cases. This paper proposes a classification system called genetic algorithm-based ensemble classification system (GA-ECS) for the identification of heart disease. As feature selection is the crucial step before applying the data mining techniques, the genetic algorithm is used in GA-ECS to identify the best features in a given dataset. The Cleveland heart disease dataset is used for testing GA-ECS. The performance of GA-ECS is compared with different machine learning classifiers for the prediction of heart disease. GA-ECS showed a promising outcome with an accuracy of 90% for the diagnosis of heart disease.

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Yan, Bowei, Xiaona Ye, Jing Wang, et al. "An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning." Molecules 27, no. 10 (2022): 3112. http://dx.doi.org/10.3390/molecules27103112.

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In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.

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ElDen, AhmedSharaf, Malaka A. Moustafa, Hany M. Harb, and Abdel H.Emara. "Adaboost Ensemble with Simple Genetic Algorithm for Student Prediction Model." International Journal of Computer Science and Information Technology 5, no. 2 (2013): 73–85. http://dx.doi.org/10.5121/ijcsit.2013.5207.

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Wang, Hongzhi, Chengquan He, and Zhuping Li. "A new ensemble feature selection approach based on genetic algorithm." Soft Computing 24, no. 20 (2020): 15811–20. http://dx.doi.org/10.1007/s00500-020-04911-x.

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Ekbal, Asif, and Sriparna Saha. "Classifier Ensemble Selection Using Genetic Algorithm for Named Entity Recognition." Research on Language and Computation 8, no. 1 (2010): 73–99. http://dx.doi.org/10.1007/s11168-010-9071-0.

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Kanász, Róbert, Peter Gnip, Martin Zoričák, and Peter Drotár. "Bankruptcy prediction using ensemble of autoencoders optimized by genetic algorithm." PeerJ Computer Science 9 (June 8, 2023): e1257. http://dx.doi.org/10.7717/peerj-cs.1257.

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The prediction of imminent bankruptcy for a company is important to banks, government agencies, business owners, and different business stakeholders. Bankruptcy is influenced by many global and local aspects, so it can hardly be anticipated without deeper analysis and economic modeling knowledge. To make this problem even more challenging, the available bankruptcy datasets are usually imbalanced since even in times of financial crisis, bankrupt companies constitute only a fraction of all operating businesses. In this article, we propose a novel bankruptcy prediction approach based on a shallow autoencoder ensemble that is optimized by a genetic algorithm. The goal of the autoencoders is to learn the distribution of the majority class: going concern businesses. Then, the bankrupt companies are represented by higher autoencoder reconstruction errors. The choice of the optimal threshold value for the reconstruction error, which is used to differentiate between bankrupt and nonbankrupt companies, is crucial and determines the final classification decision. In our approach, the threshold for each autoencoder is determined by a genetic algorithm. We evaluate the proposed method on four different datasets containing small and medium-sized enterprises. The results show that the autoencoder ensemble is able to identify bankrupt companies with geometric mean scores ranging from 71% to 93.7%, (depending on the industry and evaluation year).

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Kalaivani, P., and K. L. Shunmuganathan. "Feature Reduction Based on Genetic Algorithm and Hybrid Model for Opinion Mining." Scientific Programming 2015 (2015): 1–15. http://dx.doi.org/10.1155/2015/961454.

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With the rapid growth of websites and web form the number of product reviews is available on the sites. An opinion mining system is needed to help the people to evaluate emotions, opinions, attitude, and behavior of others, which is used to make decisions based on the user preference. In this paper, we proposed an optimized feature reduction that incorporates an ensemble method of machine learning approaches that uses information gain and genetic algorithm as feature reduction techniques. We conducted comparative study experiments on multidomain review dataset and movie review dataset in opinion mining. The effectiveness of single classifiers Naïve Bayes, logistic regression, support vector machine, and ensemble technique for opinion mining are compared on five datasets. The proposed hybrid method is evaluated and experimental results using information gain and genetic algorithm with ensemble technique perform better in terms of various measures for multidomain review and movie reviews. Classification algorithms are evaluated using McNemar’s test to compare the level of significance of the classifiers.

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Silva Fernandes, Fernando M. S. "Monte Carlo Simulation on Adiabatic Ensembles and a Genetic Algorithm." Entropy 27, no. 6 (2025): 565. https://doi.org/10.3390/e27060565.

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This paper concerns interactive Monte Carlo simulations for adiabatic ensembles and a genetic algorithm to research and educational contexts. In the Introduction, we discuss some concepts of thermodynamics, statistical mechanics and ensembles relevant to molecular simulations. The second and third sections of the paper comprise two programs in JavaScript regarding (i) argon in the grand-isobaric ensemble focusing on the direct calculation of entropy, vapor–liquid equilibria and radial distribution functions and (ii) an ideal system of quantized harmonic oscillators in the microcanonical ensemble for the determination of the entropy and Boltzmann distribution, also including the definition of Boltzmann and Gibbs entropies relative to classical systems. The fourth section is concerned with a genetic algorithm program in Java, as a pedagogical alternative to introduce the Second Law of Thermodynamics, which summarizes artificial intelligence methods and the cumulative selection process in biogenesis.

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ALIZADEH, HOSEIN, BEHROUZ MINAEI-BIDGOLI, and HAMID PARVIN. "OPTIMIZING FUZZY CLUSTER ENSEMBLE IN STRING REPRESENTATION." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 02 (2013): 1350005. http://dx.doi.org/10.1142/s0218001413500055.

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In this paper, we present a novel optimization-based method for the combination of cluster ensembles. The information among the ensemble is formulated in 0-1 bit strings. The suggested model defines a constrained nonlinear objective function, called fuzzy string objective function (FSOF), which maximizes the agreement between the ensemble members and minimizes the disagreement simultaneously. Despite the crisp primary partitions, the suggested model employs fuzzy logic in the mentioned objective function. Each row in a candidate solution of the model includes membership degrees indicating how much data point belongs to each cluster. The defined nonlinear model can be solved by every nonlinear optimizer; however; we used genetic algorithm to solve it. Accordingly, three suitable crossover and mutation operators satisfying the constraints of the problem are devised. The proposed crossover operators exchange information between two clusters. They use a novel relabeling method to find corresponding clusters between two partitions. The algorithm is applied on multiple standard datasets. The obtained results show that the modified genetic algorithm operators are desirable in exploration and exploitation of the big search space.

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Odiakaose, Christopher, Frances Emordi, Patrick Ejeh, et al. "Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection." Advances in Multidisciplinary & Scientific Research Journal Publications 12, no. 1 (2024): 37–52. http://dx.doi.org/10.22624/aims/maths/v12n1p4.

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Today’s popularity of the short messages services (SMS) has created a propitious environment for spamming to thrive. Spams are unsolicited advertising, adult-themed or inappropriate content, premium fraud, smishing and malware. They are a constant reminder of the need for an effective spam filter. However, SMS limitations of 160-charcaters and 140-bytes size as well as its being rippled with slangs, emoticons and abbreviations further inhibits effective training of models to aid accurate classification. The study proposes Genetic Algorithm Trained Bayesian Network solution that seeks to normalize noisy feats, expand text via use of lexicographic and semantic dictionaries that uses word sense disambiguation technique to train the underlying learning heuristics. And in turn, effectively help to classify SMS in spam and legitimate classes. Hybrid model comprises of text preprocessing, feature selection as well as training and classification section. Study uses a hybrid Genetic Algorithm trained Bayesian model for which the GA is used for feature selection; while, the Bayesian algorithm is used as classifier. Keywords: Hybrid Genetic Algorithm, Trained Bayesian Ensemble, Short Messages Spam Detection Odiakaose, C. Emordi, F. Ejeh, P., Ashioba, N., Odeh, C., Attoh, O. & Azaka, M. (2024): Hybrid Genetic Algorithm Trained Bayesian Ensemble for Short Messages Spam Detection. Journal of Advances in Mathematical & Computational Science. Vol. 12, No. 1. Pp 37-52. Available online at www.isteams.net/mathematics-computationaljournal. dx.doi.org/10.22624/AIMS/MATHS/V12N1P4

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Kalaivani, P., D. Logeshwari, and A. Tamizhselvi. "Sentiment Classification using Neural Network and Ensemble Model based on Genetic Algorithm." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 1885–91. https://doi.org/10.35940/ijeat.B3677.029320.

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The fast development of web sites and the number of product on these websites are available. The purpose of classification of sentiment is to efficiently identify opinion expressed in text. This paper compares three different optimized models including genetic optimized feature selection method, Genetic Algorithm (GA), ensemble approach that uses information gain and genetic algorithm as feature selection methods incorporated SVM model, Genetic Bagging (GB) and the next method uses optimized feature selection as feature selection technique incorporated back propagation model, Genetic Neural Network (GNN) models are compared. We are tested in sentiment analysis using sample multi-domain review datasets and movie review dataset.. These approaches are tested using various quality metrics and the results show that the Genetic Bagging (GB) technique outperforms in classifying the sentiment of the multi domain reviews and movie reviews. An empirical analysis is performed to compare the level of importance of the classifiers GB, GNN methods with Mc Nemar’s statistical method.

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Taha, Altyeb, and Omar Barukab. "Android Malware Classification Using Optimized Ensemble Learning Based on Genetic Algorithms." Sustainability 14, no. 21 (2022): 14406. http://dx.doi.org/10.3390/su142114406.

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The continuous increase in Android malware applications (apps) represents a significant danger to the privacy and security of users’ information. Therefore, effective and efficient Android malware app-classification techniques are needed. This paper presents a method for Android malware classification using optimized ensemble learning based on genetic algorithms. The suggested method is divided into two steps. First, a base learner is used to handle various machine learning algorithms, including support vector machine (SVM), logistic regression (LR), gradient boosting (GB), decision tree (DT), and AdaBoost (ADA) classifiers. Second, a meta learner RF-GA, utilizing genetic algorithm (GA) to optimize the parameters of a random forest (RF) algorithm, is employed to classify the prediction probabilities from the base learner. The genetic algorithm is used to optimize the parameter settings in the RF algorithm in order to obtain the highest Android malware classification accuracy. The effectiveness of the proposed method was examined on a dataset consisting of 5560 Android malware apps and 9476 goodware apps. The experimental results demonstrate that the suggested ensemble-learning strategy for classifying Android malware apps, which is based on an optimized random forest using genetic algorithms, outperformed the other methods and achieved the highest accuracy (94.15%), precision (94.15%), and area under the curve (AUC) (98.10%).

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SADEGHI, SABEREH, and HAMID BEIGY. "A NEW ENSEMBLE METHOD FOR FEATURE RANKING IN TEXT MINING." International Journal on Artificial Intelligence Tools 22, no. 03 (2013): 1350010. http://dx.doi.org/10.1142/s0218213013500103.

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Dimensionality reduction is a necessary task in data mining when working with high dimensional data. A type of dimensionality reduction is feature selection. Feature selection based on feature ranking has received much attention by researchers. The major reasons are its scalability, ease of use, and fast computation. Feature ranking methods can be divided into different categories and may use different measures for ranking features. Recently, ensemble methods have entered in the field of ranking and achieved more accuracy among others. Accordingly, in this paper a Heterogeneous ensemble based algorithm for feature ranking is proposed. The base ranking methods in this ensemble structure are chosen from different categories like information theoretic, distance based, and statistical methods. The results of the base ranking methods are then fused into a final feature subset by means of genetic algorithm. The diversity of the base methods improves the quality of initial population of the genetic algorithm and thus reducing the convergence time of the genetic algorithm. In most of ranking methods, it's the user's task to determine the threshold for choosing the appropriate subset of features. It is a problem, which may cause the user to try many different values to select a good one. In the proposed algorithm, the difficulty of determining a proper threshold by the user is decreased. The performance of the algorithm is evaluated on four different text datasets and the experimental results show that the proposed method outperforms all other five feature ranking methods used for comparison. One advantage of the proposed method is that it is independent to the classification method used for classification.

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Papageorgiou, Poczeta, Papageorgiou, Gerogiannis, and Stamoulis. "Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece." Algorithms 12, no. 11 (2019): 235. http://dx.doi.org/10.3390/a12110235.

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This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series.

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Alzanin, Samah M., Abdu Gumaei, Md Azimul Haque, and Abdullah Y. Muaad. "An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning." Applied Sciences 13, no. 18 (2023): 10264. http://dx.doi.org/10.3390/app131810264.

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Multilabel classification of Arabic text is an important task for understanding and analyzing social media content. It can enable the categorization and monitoring of social media posts, the detection of important events, the identification of trending topics, and the gaining of insights into public opinion and sentiment. However, multilabel classification of Arabic contents can present a certain challenge due to the high dimensionality of the representation and the unique characteristics of the Arabic language. In this paper, an effective approach is proposed for Arabic multilabel classification using a metaheuristic Genetic Algorithm (GA) and ensemble learning. The approach explores the effect of Arabic text representation on classification performance using both Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Moreover, it compares the performance of ensemble learning methods such as the Extra Trees Classifier (ETC) and Random Forest Classifier (RFC) against a Logistic Regression Classifier (LRC) as a single and ensemble classifier. We evaluate the approach on a new public dataset, namely, the MAWQIF dataset. The MAWQIF is the first multilabel Arabic dataset for target-specific stance detection. The experimental results demonstrate that the proposed approach outperforms the related work on the same dataset, achieving 80.88% for sentiment classification and 68.76% for multilabel tasks in terms of the F1-score metric. In addition, the data augmentation with feature selection improves the F1-score result of the ETC from 65.62% to 68.80%. The study shows the ability of the GA-based feature selection with ensemble learning to improve the classification of multilabel Arabic text.

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Ramapraba, Palayanoor Seethapathy, Moorthy Radhika, Sokkanarayanan Sumathi, Jayavarapu Karthik, and Nachiappan Senthamilarasi. "An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1698. http://dx.doi.org/10.11591/ijeecs.v34.i3.pp1698-1707.

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Cloud computing, deep learning, clustering, genetic, and ensemble algorithms in healthcare are gaining popularity. This research highlights the relevance and complex repercussions of this integration. Cloud computing is transforming healthcare by providing scalable data storage and application access. It streamlines data exchange between hospitals, researchers, and institutions. Deep learning allows healthcare systems to use artificial intelligence for diagnostics, predictive analytics, and customized medication. Clustering algorithms segment patients, improving therapy and intervention customization. Genetic algorithms can optimize healthcare processes like treatment planning and resource allocation. Ensemble algorithms combine multiple models to improve predicted accuracy, enabling strong healthcare decision-making. This connection has several benefits. Healthcare systems become more efficient and scalable, resulting in cost-effective resource allocation. Access to patient data and apps promotes collaborative research and real-time healthcare. Deep learning algorithms can recognize complex medical data patterns, improving illness diagnosis and treatment results. Clustering algorithms streamline customized healthcare by stratifying individuals by clinical variables. Genetic algorithms optimize resource allocation, assuring healthcare resource efficiency. Ensemble algorithms improve predicted accuracy and clinical decision support system dependability. Its efficiency, accessibility, and prediction accuracy are positives, but security, resource constraints, interpretability, and ethical issues are obstacles.

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Ramapraba, Palayanoor Seethapathy, Moorthy Radhika, Sokkanarayanan Sumathi, Jayavarapu Karthik, and Nachiappan Senthamilarasi. "An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm." Indonesian Journal of Electrical Engineering and Computer Science 34, no. 3 (2024): 1698–707. https://doi.org/10.11591/ijeecs.v34.i3.pp1698-1707.

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Cloud computing, deep learning, clustering, genetic, and ensemble algorithms in healthcare are gaining popularity. This research highlights the relevance and complex repercussions of this integration. Cloud computing is transforming healthcare by providing scalable data storage and application access. It streamlines data exchange between hospitals, researchers, and institutions. Deep learning allows healthcare systems to use artificial intelligence for diagnostics, predictive analytics, and customized medication. Clustering algorithms segment patients, improving therapy and intervention customization. Genetic algorithms can optimize healthcare processes like treatment planning and resource allocation. Ensemble algorithms combine multiple models to improve predicted accuracy, enabling strong healthcare decision-making. This connection has several benefits. Healthcare systems become more efficient and scalable, resulting in cost-effective resource allocation. Access to patient data and apps promotes collaborative research and real-time healthcare. Deep learning algorithms can recognize complex medical data patterns, improving illness diagnosis and treatment results. Clustering algorithms streamline customized healthcare by stratifying individuals by clinical variables. Genetic algorithms optimize resource allocation, assuring healthcare resource efficiency. Ensemble algorithms improve predicted accuracy and clinical decision support system dependability. Its efficiency, accessibility, and prediction accuracy are positives, but security, resource constraints, interpretability, and ethical issues are obstacles.

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Deshpande, Apoorva, and Ramnaresh Sharma. "Anomaly Detection using Optimized Features using Genetic Algorithm and MultiEnsemble Classifier." IJOSTHE 5, no. 6 (2018): 7. http://dx.doi.org/10.24113/ojssports.v5i6.79.

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Anomaly detection system plays an important role in network security. Anomaly detection or intrusion detection model is a predictive model used to predict the network data traffic as normal or intrusion. Machine Learning algorithms are used to build accurate models for clustering, classification and prediction. In this paper classification and predictive models for intrusion detection are built by using machine learning classification algorithms namely Random Forest. These algorithms are tested with KDD-99 data set. In this research work the model for anomaly detection is based on normalized reduced feature and multilevel ensemble classifier. The work is performed in divided into two stages. In the first stage data is normalized using mean normalization. In second stage genetic algorithm is used to reduce number of features and further multilevel ensemble classifier is used for classification of data into different attack groups. From result analysis it is analysed that with reduced feature intrusion can be classified more efficiently.

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Kuroda, Kazuma. "Predicting Optimal Trading Actions Using a Genetic Algorithm and Ensemble Method." Intelligent Information Management 09, no. 06 (2017): 229–35. http://dx.doi.org/10.4236/iim.2017.96012.

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Xu, Rongwu, and Lin He. "GACEM: Genetic Algorithm Based Classifier Ensemble in a Multi-sensor System." Sensors 8, no. 10 (2008): 6203–24. http://dx.doi.org/10.3390/s8106203.

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Alfred, Rayner, Gabriel Jong Chiye, Joe Henry Obit, Mohd Hanafi Ahmad Hijazi, Kim On Chin, and HuiKeng Lau. "A Genetic Algorithm Based Clustering Ensemble Approach to Learning Relational Databases." Advanced Science Letters 21, no. 10 (2015): 3313–17. http://dx.doi.org/10.1166/asl.2015.6477.

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Zhang, Dabin, Chaomin Cai, Shanying Chen, and Liwen Ling. "An improved genetic algorithm for optimizing ensemble empirical mode decomposition method." Systems Science & Control Engineering 7, no. 2 (2019): 53–63. http://dx.doi.org/10.1080/21642583.2019.1627598.

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Rahman, Ashfaqur, and Brijesh Verma. "Ensemble classifier generation using non-uniform layered clustering and Genetic Algorithm." Knowledge-Based Systems 43 (May 2013): 30–42. http://dx.doi.org/10.1016/j.knosys.2013.01.002.

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Arowolo, Micheal Olaolu, Marion O. Adebiyi, Ayodele A. Adebiyi, and Olatunji J. Okesola. "Predicting RNA-seq data using genetic algorithm and ensemble classification algorithms." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 1073. http://dx.doi.org/10.11591/ijeecs.v21.i2.pp1073-1081.

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<p>Malaria parasites accept uncertain, inconsistent life span breeding through vectors of mosquitoes stratospheres. Thousands of different transcriptome parasites exist. A prevalent ribonucleic acid sequencing (RNA-seq) technique for gene expression has brought about enhanced identifications of genetical queries. Computation of RNA-seq gene expression data transcripts requires enhancements using analytical machine learning procedures. Numerous learning approaches have been adopted for analyzing and enhancing the performance of biological data and machines. In this study, a genetic algorithm dimensionality reduction technique is proposed to fetch relevant information from a huge dimensional RNA-seq dataset, and classification uses Ensemble classification algorithms. The experiment is performed using a mosquito Anopheles gambiae dataset with a classification accuracy of 81.7% and 88.3%.</p>

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Gaikwad, D. P., and S. V. Chaitanya. "Grading Method of Ensemble and Genetic Algorithm for Intrusion Detection System." SAMRIDDHI : A Journal of Physical Sciences, Engineering and Technology 14, Spl-2 issu (2022): 262–70. http://dx.doi.org/10.18090/samriddhi.v14spli02.11.

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Intrusion Detection System is very important tool for network security. However, Intrusion Detection System suffers from the problem of handling large volume of data and produces high false positive rate. In this paper, a novel Grading method of ensemble has proposed to overcome limitation of intrusion detection system. Partial decision tree (PART), RIpple DOwn Rule (RIDOR) learner and J48 decision tree have used as base classifiers of Grading classifier. Optimzed Genetic Search algorithm have used for selection of features.These three base classifiers have graded using RandomForest decision tree as a Meta classifier. Experimental results show that the proposed Grading method of classification offers accuracies of 81.3742%, 99.9159% and 99.8023% on testing, training datasets and cross validation respectively. It is found that the proposed graded classifier outperform its base classifiers and existing hybrid intrusion detection system in term of accuracy, false positive rate and model building time.

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Arowolo, Micheal Olaolu, Marion O. Adebiyi, Ayodele A. Adebiyi, and Olatunji J. Okesola. "Predicting RNA-Seq data using genetic algorithm and ensemble classification algorithms." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 2 (2021): 1073–81. https://doi.org/10.11591/ijeecs.v21.i2.pp1073-1081.

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Malaria parasites accept uncertain, inconsistent life span breeding through vectors of mosquitoes stratospheres. Thousands of different transcriptome parasites exist. A prevalent Ribonucleic acid sequencing (RNA-seq) technique for gene expression has brought about enhanced identifications of genetical queries. Computation of RNA-seq gene expression data transcripts requires enhancements using analytical machine learning procedures. Numerous learning approaches have been adopted for analyzing and enhancing the performance of biological data and machines. In this study, a Genetic algorithm dimensionality reduction technique is proposed to fetch relevant information from a huge dimensional RNA-seq dataset, and classification uses Ensemble classification algorithms. The experiment is performed using a mosquito Anopheles gambiae dataset with a classification accuracy of 81.7% and 88.3%.

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Gu, Zhongyuan, Miaocong Cao, Chunguang Wang, Na Yu, and Hongyu Qing. "Research on Mining Maximum Subsidence Prediction Based on Genetic Algorithm Combined with XGBoost Model." Sustainability 14, no. 16 (2022): 10421. http://dx.doi.org/10.3390/su141610421.

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The extreme gradient boosting (XGBoost) ensemble learning algorithm excels in solving complex nonlinear relational problems. In order to accurately predict the surface subsidence caused by mining, this work introduces the genetic algorithm (GA) and XGBoost integrated algorithm model for mining subsidence prediction and uses the Python language to develop the GA-XGBoost combined model. The hyperparameter vector of XGBoost is optimized by a genetic algorithm to improve the prediction accuracy and reliability of the XGBoost model. Using some domestic mining subsidence data sets to conduct a model prediction evaluation, the results show that the R2 (coefficient of determination) of the prediction results of the GA-XGBoost model is 0.941, the RMSE (root mean square error) is 0.369, and the MAE (mean absolute error) is 0.308. Then, compared with classic ensemble learning models such as XGBoost, random deep forest, and gradient boost, the GA-XGBoost model has higher prediction accuracy and performance than a single machine learning model.

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Shrikant, Vanve* Prof. Sarita Patil. "OGEDIDS: OPPOSITIONAL GENETIC PROGRAMMING ENSEMBLE FOR DISTRIBUTED INTRUSION DETECTION SYSTEMS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 7 (2016): 756–62. https://doi.org/10.5281/zenodo.57737.

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Due to the wide range application of internet and computer networks, the securing of information is indispensable one. In order to secure the information system more effectively, various distributed intrusion detection has been developed in the literature. In this paper, we utilize the oppositional genetic algorithm for Distributed Network Intrusion Detection utilizing the oppositional set based population selection mechanism. This system is mostly useful for detecting unauthorized & malicious attack in distributed network. Here, Oppositional genetic algorithm (OGA) is utilized in OGA ensemble for learning the intrusion detection behavior of networks. Also, OGA ensemble is adapted for distributed intrusion detection system by creating the network profile which classifies normal and abnormal behavior of network. For experimentation, network profile contains different classifier which uses training data set of KDD Cup 99 to generate intrusion rules. For validation, we utilize the confusion matrix, sensitivity, specificity and accuracy and the results are proved that the proposed OGEdIDS are better for intrusion detection

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Ruano, Antonio, and Maria da Graça Ruano. "Designing Robust Forecasting Ensembles of Data-Driven Models with a Multi-Objective Formulation: An Application to Home Energy Management Systems." Inventions 8, no. 4 (2023): 96. http://dx.doi.org/10.3390/inventions8040096.

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This work proposes a procedure for the multi-objective design of a robust forecasting ensemble of data-driven models. Starting with a data-selection algorithm, a multi-objective genetic algorithm is then executed, performing topology and feature selection, as well as parameter estimation. From the set of non-dominated or preferential models, a smaller sub-set is chosen to form the ensemble. Prediction intervals for the ensemble are obtained using the covariance method. This procedure is illustrated in the design of four different models, required for energy management systems. Excellent results were obtained by this methodology, superseding the existing alternatives. Further research will incorporate a robustness criterion in MOGA, and will incorporate the prediction intervals in predictive control techniques.

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Golshanrad, Paria, Hossein Rahmani, Banafsheh Karimian, Fatemeh Karimkhani, and Gerhard Weiss. "MEGA: Predicting the best classifier combination using meta-learning and a genetic algorithm." Intelligent Data Analysis 25, no. 6 (2021): 1547–63. http://dx.doi.org/10.3233/ida-205494.

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Classifier combination through ensemble systems is one of the most effective approaches to improve the accuracy of classification systems. Ensemble systems are generally used to combine classifiers; However, selecting the best combination of individual classifiers is a challenging task. In this paper, we propose an efficient assembling method that employs both meta-learning and a genetic algorithm for the selection of the best classifiers. Our method is called MEGA, standing for using MEta-learning and a Genetic Algorithm for algorithm recommendation. MEGA has three main components: Training, Model Interpretation and Testing. The Training component extracts meta-features of each training dataset and uses a genetic algorithm to discover the best classifier combination. The Model Interpretation component interprets the relationships between meta-features and classifiers using a priori and multi-label decision tree algorithms. Finally, the Testing component uses a weighted k-nearest-neighbors algorithm to predict the best combination of classifiers for unseen datasets. We present extensive experimental results that demonstrate the performance of MEGA. MEGA achieves superior results in a comparison of three other methods and, most importantly, is able to find novel interpretable rules that can be used to select the best combination of classifiers for an unseen dataset.

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Journal articles on the topic 'Genetic algorithm basedselected ensemble'

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