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Comprehensive approach for solving multimodal data analysis problems based on integration of evolutionary, neural and deep neural network algorithms

I Ivanov, E Sopov, I Panfilov
2018 IOP Conference Series: Materials Science and Engineering  
This approach involves multimodal data fusion techniques, multi-objective approach to feature selection and neural network ensemble optimization, as well as convolutional neural networks trained with hybrid  ...  learning algorithm that includes consecutive use of the genetic optimization algorithm and the back-propagation algorithm.  ...  Acknowledgements The research is performed with the financial support of the Ministry of Education and Science of the Russian Federation within the State Assignment for the Siberian State Aerospace University  ... 
doi:10.1088/1757-899x/450/5/052007 fatcat:uaqlkkxwovfh3nfxa4w24dgy6i

Design of Ensemble Classifier Model Based on MLP Neural Network For Breast Cancer Diagnosis

Amin Rezaeipanah, Neda Boroumand
2021 Inteligencia Artificial  
In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimized with an Evolutionary Algorithm (EA) for maximize the classification  ...  In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis.  ...  MLP neural network Multi-Layer Perceptron Neural Networks (MLP-NNs) are supervised learning-based feed-forward neural networks used to solve regression and classification problems [6] .  ... 
doi:10.4114/intartif.vol24iss67pp147-156 fatcat:df473tyvmndznkpug3nqb3kxi4

Evolutionary Machine Learning: A Survey

Akbar Telikani, Amirhessam Tahmassebi, Wolfgang Banzhaf, Amir H. Gandomi
2022 ACM Computing Surveys  
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  ...  We look at the contributions of EC to ML in nine sub-fields: feature selection, resampling, classifiers, neural networks, reinforcement learning, clustering, association rule mining, and ensemble methods  ...  [130, 131] published a two-part survey discussing recent developments in multi-objective evolutionary algorithms for data mining problems such as feature selection, classification, clustering, and ARM  ... 
doi:10.1145/3467477 fatcat:o6m3nekqfnaudjnxxoeferhine

Table of Contents

2020 2020 IEEE Symposium Series on Computational Intelligence (SSCI)  
Test Case Prioritization Lukas Rosenbauer, Anthony Stein, David Paetzel and Joerg Haehner .......... 1307 A Multi-objective Evolutionary Algorithm based on R2 Indicator for Pickup and Delivery Problem  ...  ... 554 CIMD1: Machine Learning, Chair: Zhen Ni Gregory Ditzler Multi-objective Evolutionary Top Rank Optimization with Pareto Ensemble Kai Wu and JIng Liu .......... 624 xxi Wenshuai Zhao, Jorge  ... 
doi:10.1109/ssci47803.2020.9308155 fatcat:hyargfnk4vevpnooatlovxm4li

A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [article]

Alejandro Morales-Hernández and Inneke Van Nieuwenhuyse and Sebastian Rojas Gonzalez
2021 arXiv   pre-print
This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristic-based algorithms, metamodel-based algorithms  ...  We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.  ...  Joint model for feature selection and parameter optimization coupled with classifier ensemble in chemical mention recog- nition. Knowledge-Based Systems, 85 , 37–51.  ... 
arXiv:2111.13755v2 fatcat:q2qtofihtzev5mose5aj7odfzm

Special issue on Hybridization of Intelligent Systems

M. Köppen, R. Weber, M. Köppen, R. Weber
2007 International Journal of Hybrid Intelligent Systems  
In the paper "Genetic Rule Selection with a Multi-Classifier Coding Scheme for Ensemble Classifier Design" by Y. Nojima and H.  ...  for classifier diversity as an additional objective, and apply multi-objective optimization.  ... 
doi:10.3233/his-2007-4301 fatcat:t6gzqybrsjeeflpvalirmz456u


Deepika P, Saranya S, Dr.Sasikala S
2019 EPRA international journal of research & development  
KEYWORDS: Data mining, Classification, Particle Swarm Optimization, Cardiovascular Disease(CVD).  ...  Data mining plays a major role in the construction of an intellectual prediction model for healthcare system to detect Heart Disease (HD) using patient data sets, which support doctors in diminishing mortality  ...  Majid Ghonji Feshki et al[11] 2016 Improving the Heart Disease Diagnosis by Evolutionary Algorithm of PSO and Feed Forward Neural Network Particle Swarm Optimization along with Neural Network  ... 
doi:10.36713/epra3747 fatcat:2eronako5zh55nhg6b4vlix6jm

Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs

Cristian Rotaru, Octav Brudaru
2012 2012 IEEE Congress on Evolutionary Computation  
This paper presents a cellular genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The evolution process is inspired by a basic genetic algorithm.  ...  The approach systematically produces better results than the used basic genetic algorithm and better or similar results with other heuristic methods.  ...  Experimental Study for Multi-Objective PSO with Single Objective Guide Selection 459, Jenn-Long Liu, Yu-Tzu Hsu and Chih-Lung Hung, Development of Evolutionary Data Mining Algorithms and their Applications  ... 
doi:10.1109/cec.2012.6256590 dblp:conf/cec/RotaruB12 fatcat:4ly3nrktw5habc6lf5err7d5py

An Improved Particle Swarm Optimization based classification model for high dimensional medical disease prediction

P R.Sudha Rani, Dr K.Kiran Kumar
2018 International Journal of Engineering & Technology  
The main objective of the feature selection based hybrid classifier is to classify the high dimensional data for large medical feature set.  ...  In this paper, a novel particle swarm optimization based hybrid classifier was implemented for medical disease prediction with high dimensions.  ...  Conclusion In this paper, an optimized PSO feature selection method is integrated with the weighted ELM model for ensemble learning on microarray datasets.  ... 
doi:10.14419/ijet.v7i2.7.10880 fatcat:edvudekixzcflc2t2jlf4gywby

Multiobjective Genetic Fuzzy Systems: Review and Future Research Directions

Hisao Ishibuchi
2007 IEEE International Fuzzy Systems conference proceedings  
Recently evolutionary multiobjective algorithms have been used for interpretability-accuracy tradeoff analysis of fuzzy systems.  ...  Evolutionary algorithms have been successfully used in many studies to design accurate and interpretable fuzzy systems under the name of genetic fuzzy systems.  ...  Feature selection was used for neural network ensemble design in [88] , [89] . G.  ... 
doi:10.1109/fuzzy.2007.4295487 dblp:conf/fuzzIEEE/Ishibuchi07 fatcat:wjbozagl6zevrcauiklvctmd7y

Table of Contents

2021 2021 IEEE Congress on Evolutionary Computation (CEC)  
SS3: Evolutionary Computation for Feature Selection, Extraction and Dimensionality Reduction Chair: Bach Nguyen Particle Swarm Optimization for Feature Selection in Emotion Categorization 752 Multi-objective  ...  Nowaczyk, Sepideh Pashami and Peyman Sheikholharam Mashhadi Halmstad University, Sweden A Forward Search Inspired Particle Swarm Optimization Algorithm for Feature Selection in Classification 786  ... 
doi:10.1109/cec45853.2021.9504744 fatcat:gnwg3ppxbbga3ceypy5sc666xi

Multi-layer heterogeneous ensemble with classifier and feature selection

Tien Thanh Nguyen, Nang Van Pham, Manh Truong Dang, Anh Vu Luong, John McCall, Alan Wee Chung Liew
2020 Proceedings of the 2020 Genetic and Evolutionary Computation Conference  
The selection method uses NSGA-II algorithm to optimize two objectives concerning classification accuracy and ensemble diversity.  ...  We also propose an Evolutionary Algorithm-based selection method to select the subset of suitable classifiers and features at each layer to enhance the predictive performance of MULES.  ...  We used NSGA-II, a popular and effective multi-objective evolutionary algorithm, to solve this optimization problem.  ... 
doi:10.1145/3377930.3389832 dblp:conf/gecco/NguyenPDLML20 fatcat:pu465t544zhbvbjej5rxtd5jtm

Optimal ensemble construction via meta-evolutionary ensembles

2006 Expert systems with applications  
Ensembles consisting of multiple classifiers also compete for member classifiers, and are rewarded based on their predictive performance.  ...  In this paper we propose a meta-evolutionary approach to improve on the performance of individual classifiers.  ...  This evolutionary local selection algorithm (ELSA) has been successfully applied to multi-objective optimization problems, such as feature selection in both supervised and unsupervised learning [14] [  ... 
doi:10.1016/j.eswa.2005.07.030 fatcat:5ctgm5gtfzbldb7w7hlagefc3e

Clustering and co-evolution to construct neural network ensembles: An experimental study

Fernanda L. Minku, Teresa B. Ludermir
2008 Neural Networks  
The experiments performed with seven classification databases and three different co-evolutionary algorithms show that CONE considerably reduces the execution time without prejudicing (and even improving  ...  This paper introduces an approach called Clustering and Co-evolution to Construct Neural Network Ensembles (CONE).  ...  Acknowledgments The first author would like to thank the Federal University of Pernambuco (Brazil), the Brazilian Institute of Technology for Development (LACTEC), the Brazilian Technological and Scientific  ... 
doi:10.1016/j.neunet.2008.02.001 pmid:18378116 fatcat:nxwqfwjsnreejemebjnsralyxy

Memetic Pareto Differential Evolutionary Neural Network for Donor-Recipient Matching in Liver Transplantation [chapter]

M. Cruz-Ramírez, C. Hervás-Martínez, P. A. Gutiérrez, J. Briceño, M. de la Mata
2011 Lecture Notes in Computer Science  
Therefore, a Multi-Objective Evolutionary Algorithm and various techniques of selection of individuals are used in this paper to obtain Artificial Neural Network models to assist in making decisions.  ...  Donor-Recipient matching constitutes a complex scenario not easily modelable. The risk of subjectivity and the likelihood of falling into error must not be underestimated.  ...  These models are obtained by a multi-objective evolutionary algorithm where Accuracy is the measure considered to evaluate model performance along with the Minimum Sensitivity measure.  ... 
doi:10.1007/978-3-642-21498-1_17 fatcat:43imcimzibdxdj2y4lhuvwycje
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