Search Results - (( using classification problems algorithm ) OR ( evolution identification using algorithm ))
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Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…ARDE algorithm makes use of JADE strategy and the MDE_pBX parameters adaptive schemes as frameworks. …”
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Voltage stability margin identification using evolution programming learning algorithm / Zamzuhairi Darus
Published 2003“…This project proposed on an investigation on the voltage stability margin identification using evolution programming learning algorithm. …”
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Hybrid algorithm for NARX network parameters' determination using differential evolution and genetic algorithm
Published 2013“…A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). …”
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Effective EEG channels for emotion identification over the brain regions using differential evolution algorithm
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Hybrid DE-PEM algorithm for identification of UAV helicopter
Published 2014“…Purpose – The purpose of this paper is to develop a hybrid algorithm using differential evolution (DE) and prediction error modeling (PEM) for identification of small-scale autonomous helicopter state-space model. …”
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Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
Published 2024“…The unified RBF model is then used for autotuning the PID controller using the DE algorithm. …”
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Dengue classification system using clonal selection algorithm / Karimah Mohd
Published 2012“…This project can be improved by making a comparative study on Artificial Immune System and other techniques or algorithms used to solve dengue classification problems.…”
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Autotuning PID Controllers for Quadplane Hybrid UAV using Differential Evolution Algorithm
Published 2024“…The unified RBF model is then used for autotuning the PID controller using the DE algorithm. …”
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Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution
Published 2014“…This study proposes a hybrid of conventional back propagation training algorithm for the NARX network and multiobjective differential evolution (MODE) algorithm for identification of a nonlinear model of an unmanned small scale helicopter from experimental flight data.The proposed hybrid algorithm was able to produce models with Pareto-optimal compromise between the design objectives. …”
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An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning focusing on predicting class labels for datasets with continuous features. …”
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An improve unsupervised discretization using optimization algorithms for classification problems
Published 2024“…This paper addresses the classification problem in machine learning, focusing on predicting class labels for datasets with continuous features. …”
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Classification of breast cancer disease using bagging fuzzy-id3 algorithm based on fuzzydbd
Published 2022“…One of the most powerful machine learning methods to handle classification problems is the decision tree. There are various decision tree algorithms, but the most commonly used are Iterative Dichotomiser 3 (ID3), CART, and C4.5. …”
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A Hybrid of Ant Colony Optimization Algorithm and Simulated Annealing for Classification Rules
Published 2013“…Classification rule induction is one of the problems solved by the Ant-miner algorithm, a variant of ACO, which was initiated by Parpinelli in 2001. …”
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Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…ACO originally deals with discrete optimization problem.In applying ACO for solving SVM model selection problem which are continuous variables, there is a need to discretize the continuously value into discrete values.This discretization process would result in loss of some information and hence affects the classification accuracy and seeking time.In this algorithm we propose to solve SVM model selection problem using IACOR without the need to discretize continuous value for SVM.The second algorithm aims to simultaneously solve SVM model selection problem and selects a small number of features.SVM model selection and selection of suitable and small number of feature subsets must occur simultaneously because error produced from the feature subset selection phase will affect the values of SVM model selection and result in low classification accuracy.In this second algorithm we propose the use of IACOMV to simultaneously solve SVM model selection problem and features subset selection.Ten benchmark datasets were used to evaluate the proposed algorithms.Results showed that the proposed algorithms can enhance the classification accuracy with small size of features subset.…”
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Classification for large number of variables with two imbalanced groups
Published 2020“…Several approaches have been devoted to study such problems using linear and non-linear classification rules, but limited to group imbalance rather than the combination of both problems. …”
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Overview of metaheuristic: classification of population and trajectory
Published 2010“…Algorithms are used to find the solutions through the computer program. …”
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An adaptive ant colony optimization algorithm for rule-based classification
Published 2020“…Ant-Miner is a variant of ant colony optimisation and a prominent intelligent algorithm widely use in rules-based classification. …”
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Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…Expectation maximization (EM) is one of the representatives clustering algorithms which have broadly applied in solving classification problems by improving the density of data using the probability density function. …”
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Attribute related methods for improvement of ID3 Algorithm in classification of data: A review
Published 2020“…Decision tree is an important method in data mining to solve the classification problems. There are several learning algorithms to implement the decision tree but the most commonly-used is ID3 algorithm. …”
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