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Multi-Objective Evolutionary Instance Selection for Regression Tasks

Mirosław Kordos, Krystian Łapa
2018 Entropy  
In this work, we analyze instance selection in regression tasks and apply the NSGA-II multi-objective evolutionary algorithm to direct the search for the optimal subset of the training dataset and the  ...  k-NN algorithm for evaluating the solutions during the selection process.  ...  Multi-Objective Evolutionary Instance Selection for Regression (MEISR) This section introduces our solution to instance selection in regression tasks called MEISR.  ... 
doi:10.3390/e20100746 pmid:33265835 fatcat:66s4pka3wjhptpaztjqdirnut4

Evolutionary prototype selection for multi-output regression

Mirosław Kordos, Álvar Arnaiz-González, César García-Osorio
2019 Neurocomputing  
A novel approach to prototype selection for multi-output regression data sets is presented.  ...  A multiobjective evolutionary algorithm is used to evaluate the selections using two criteria: training data set compression and prediction quality expressed in terms of root mean squared error.  ...  Evolutionary prototype selection for multi-output regression (EPS-MOR) In this section, EPS-MOR, the proposed evolutionary method of prototype selection for multi-output regression is presented.  ... 
doi:10.1016/j.neucom.2019.05.055 fatcat:6wqyfg5bzjbobew3gxkgcqbtpy

Multi-Objective Evolutionary Simultaneous Feature Selection and Outlier Detection for Regression

F. Jimenez, E. Lucena-Sanchez, G. Sanchez, G. Sciavicco
2021 IEEE Access  
MULTI-OBJECTIVE EVOLUTIONARY OPTIMIZATION A.  ...  The first evolutionary approach that involves multi-objective optimization for feature selection was proposed in [65] with three criteria: accuracy, number of features, and number of instances.  ... 
doi:10.1109/access.2021.3115848 fatcat:oub6cpdtvrcjdns5y3weqwa6zq

A Survey on Computational Intelligence-based Transfer Learning [article]

Mohamad Zamini, Eunjin Kim
2022 arXiv   pre-print
The goal of transfer learning (TL) is providing a framework for exploiting acquired knowledge from source to target data.  ...  This paper studies computational intelligence-based transfer learning techniques and categorizes them into neural network-based, evolutionary algorithm-based, swarm intelligence-based and fuzzy logic-based  ...  In [33] , de Lima Mendes, designed support vector regression predictor along with a multi-objective evolutionary algorithm based on decomposition (MOEA/D) for a dynamic multi-objective optimization  ... 
arXiv:2206.10593v1 fatcat:n4bofmrgs5eidciu6b3p3gcxey

Optimizing Machine Learning Models using Multiobjective Grasshopper Optimization Algorithm

Ashish Sharma, Deepak Gupta, Nimish Verma, Mayank Sehgal, Nitesh
2019 Zenodo  
In the first section of this paper, theoretical foundation of multi-objective problems, feature selection and evolutionary algorithms is introduced.  ...  In the paper, MOGOA, which is a population based method has been used for feature selection.  ...  Recent reviews (from 2000 to 2014) on feature selection using multi-objective evolutionary algorithm (MOEA) has been presented in references [4] and [5] .  ... 
doi:10.5281/zenodo.4743516 fatcat:eziwo4k3gbavhanu3t53vbf6zq

Evolutionary Dynamic Multi-objective Optimization Via Regression Transfer Learning [article]

Zhenzhong Wang, Min Jiang, Xing Gao, Liang Feng, Weizhen Hu, Kay Chen Tan
2019 arXiv   pre-print
In this paper, a novel transfer learning based dynamic multi-objective optimization algorithm (DMOA) is proposed called regression transfer learning prediction based DMOA (RTLP-DMOA).  ...  Then, with the assistance of this prediction model, some high-quality solutions with better predicted objective values are selected as the initial population, which can improve the performance of the evolutionary  ...  In [20] , the authors introduce TrAdaboost-based algorithms for transfer regression task, called TrAdaboost.R2.  ... 
arXiv:1910.08753v2 fatcat:dsoudbbqwnexvfiyh6yalxabxu

Evolutionary model tree induction

Rodrigo C. Barros, Márcio P. Basgalupp, Duncan D. Ruiz, André C. P. L. F. de Carvalho, Alex A. Freitas
2010 Proceedings of the 2010 ACM Symposium on Applied Computing - SAC '10  
Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems.  ...  Evolutionary Model Tree Induction Model trees are a particular case of decision trees employed to solve regression problems, where the variable to be predicted is continuous.  ...  Evolutionary Model Tree Induction (E-Motion) E-Motion is a novel multi-objective genetic programming algorithm for model trees induction.  ... 
doi:10.1145/1774088.1774327 dblp:conf/sac/BarrosBRCF10 fatcat:jygkpgrfujhvdhpzigbez3m6xq

A multi-objective evolutionary approach to Pareto-optimal model trees

Marcin Czajkowski, Marek Kretowski
2018 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
This paper discusses the multi-objective evolutionary approach to induction of model trees. The model tree is a particular case of a decision tree designed to solve regression problems.  ...  The GMT framework can be used for the evolutionary induction of different types of decision trees, including univariate, oblique or mixed; regression and model trees.  ...  Pareto-optimal search in GMT In this section, we present our multi-objective approach for evolutionary induced regression and model trees.  ... 
doi:10.1007/s00500-018-3646-3 fatcat:hzwxaqlycbg35itds6kitdzegi

Evolutionary Machine Learning: A Survey

Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, Amir H. Gandomi
2022 ACM Computing Surveys  
For each category, we discuss evolutionary machine learning in terms of three aspects: problem formulation, search mechanisms, and fitness value computation.  ...  Evolutionary approaches can be used in all three parts of ML: preprocessing (e.g., feature selection and resampling), learning (e.g., parameter setting, membership functions, and neural network topology  ...  The method of selecting a final solution is one of the most important tasks in multi-objective ML.  ... 
doi:10.1145/3467477 fatcat:o6m3nekqfnaudjnxxoeferhine

A Survey on Evolutionary Computation Approaches to Feature Selection

Bing Xue, Mengjie Zhang, Will N. Browne, Xin Yao
2016 IEEE Transactions on Evolutionary Computation  
Multi-Objective Feature Selection Most of the existing evolutionary multi-objective (EMO) algorithms are designed for continuous problems [244] , but feature selection is a discrete problem.  ...  since feature selection is a complex task that requires specifically designed multi-objective GAs to search for the non-dominated solutions.  ... 
doi:10.1109/tevc.2015.2504420 fatcat:wj2zddgcwncgtf6kphvhyd5pq4

Multi-Task Optimization and Multi-Task Evolutionary Computation in the Past Five Years: A Brief Review

Qingzheng Xu, Na Wang, Lei Wang, Wei Li, Qian Sun
2021 Mathematics  
tasks, thereby potentially accelerating convergence and improving the quality of solutions for multi-task optimization problems.  ...  Inspired by this concept, the paradigm of multi-task evolutionary computation (MTEC) has recently emerged as an effective means of facilitating implicit or explicit knowledge transfer across optimization  ...  [117] proposed an evolutionary multi-task algorithm with learning task relationships (LTR) for the MOO problem.  ... 
doi:10.3390/math9080864 fatcat:nnkdm4zkwvaxveh5cvblhbk5ve

To the special Issue on "Metaheuristics for optimization of complex process engineering"

Jinliang Ding, Yaochu Jin
2016 Natural Computing  
A scheduling optimization model considering the communication time between tasks is formulated, with its objective being to minimize the execution time of all workflow instances.  ...  ., is titled ''Solving dynamic economic emission dispatch problem considering wind power by multi-objective differential evolution with ensemble of selection method''.  ... 
doi:10.1007/s11047-016-9578-x fatcat:rspylnm6jne7jlkho5okzx5r5e

ECoFFeS: A Software Using Evolutionary Computation for Feature Selection in Drug Discovery

Zhi-Zhong Liu, Jia-Wei Huang, Yong Wang, Dong-Sheng Cao
2018 IEEE Access  
Specifically, ECoFFeS considers both single-objective and multi-objective evolutionary algorithms, and both regression-and classification-based models to meet different requirements.  ...  for feature selection.  ...  feature selection of drug discovery. • Both single-objective evolutionary algorithms (SOEAs) and multi-objective evolutionary algorithms (MOEAs), and both regression-and classification-based models are  ... 
doi:10.1109/access.2018.2821441 fatcat:y35r5vx5kfacfle3scqmp2zobi

An Implementation of genetic algorithm based feature selection approach over medical datasets

Dr.Shaik Abdul Khadir A, Mohamed Amanullah K
2017 International Journal of Engineering and Technology  
Kashyap et al. [2] have proposed a multi objective genetic algorithm for feature selection with the objective of maximizing the Laplacian score which aims at analyzing the importance or relevance of features  ...  task for enhancing the outcome of facts.  ...  The current research of feature selection focuses on the evolutionary based attribute selection so as to increase the accuracy of prediction.  ... 
doi:10.21817/ijet/2017/v9i3/170903515 fatcat:c46vkxhzffdobbmoki3qrcacqq

A Computational Intelligence Approach for Ranking Risk Factors in Preterm Birth

Daniela Zaharie, Stefan Holban, Diana Lungeanu, Dan Navolan
2007 2007 4th International Symposium on Applied Computational Intelligence and Informatics  
The aim of this paper is to propose a filter, based on a multi-objective evolutionary algorithm, for attributes' ranking in the context of a data mining task.  ...  The results obtained by applying the proposed evolutionary approach are compared with rankings obtained by applying some classical attributes selection methods and a logistic regression procedure.  ...  From the large plethora of multi-objective evolutionary algorithms, the variant we chose was NSGA-II (Nondominated Sorting Genetic Algorithm) [7] .  ... 
doi:10.1109/saci.2007.375498 dblp:conf/saci/ZaharieHLN07 fatcat:uocxussx6bfd7e2frwmnnkdwda
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