Search Results - (( developing evolution optimization algorithm ) OR ( data classification learning algorithm ))
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1
A refined differential evolution algorithm for improving the performance of optimization process
Published 2011“…Among the latest Evaluation Algorithm (EA) have been developed is Differential Evolution (DE). …”
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2
Case Slicing Technique for Feature Selection
Published 2004“…One of the problems addressed by machine learning is data classification. Finding a good classification algorithm is an important component of many data mining projects. …”
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3
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…Whereas for supervised learning method, it requires teacher or prior data (i.e. large, prohibitive and labelled training data) during classification process which in real life, the cost of obtaining sufficient labelled training data is high. …”
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4
A new modified differential evolution algorithm scheme-based linear frequency modulation radar signal de-noising
Published 2015“…The main intention of this study was to investigate the development of a new optimization technique based on the differential evolution (DE) algorithm, for the purpose of linear frequency modulation radar signal de-noising. …”
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5
Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…In data mining, classification learning is broadly categorized into two categories; supervised and unsupervised. …”
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Hybrid algorithm for NARX network parameters' determination using differential evolution and genetic algorithm
Published 2013“…The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. …”
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7
The Development Of A Robust Algorithm For Uav Path Planning In 3d Environment
Published 2016“…Thus, a new algorithm known as infection evolution (IE) was developed in this study. …”
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8
Semi-supervised learning for feature selection and classification of data / Ganesh Krishnasamy
Published 2019“…Feature selection and classification are widely utilized for data analysis. …”
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9
Application of swarm intelligence optimization on bio-process problems / Mohamad Zihin Mohd Zain
Published 2018“…Also, fed batch fermentation problems in winery wastewater treatment and biogas generation from sewage sludge are developed for optimization. Though DE traditionally performs better than other evolutionary algorithms and swarm intelligence techniques in optimization of fed-batch fermentation, BSA edged DE and other recent metaheuristics to emerge as superior optimization method in this work. …”
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10
Multiobjective optimization of bioethanol production via hydrolysis using hopfield- enhanced differential evolution
Published 2014“…In this chapter, the weighted sum scalarization approach is used in conjunction with three meta-heuristic algorithms: Differential Evolution (DE), Hopfield-Enhanced Differential Evolution (HEDE), and Gravitational Search Algorithm (GSA). …”
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11
Grid-Based Classifier as a Replacement for Multiclass Classifier in a Supervised Non-Parametric Approach
Published 2009“…The two algorithms were compared, and the proposed algorithm was found to be able to both learn and classify quickly. …”
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12
A holistic review on artificial intelligence techniques for well placement optimization problem
Published 2020“…Nature-inspired gradient-free optimization algorithms like particle swarm optimization, genetic algorithm, covariance matrix adaptation evolution strategy and differential evolution have been utilized in this area. …”
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13
New Learning Models for Generating Classification Rules Based on Rough Set Approach
Published 2000“…Two different models for learning in data sets were proposed based on two different reduction algorithms. …”
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14
Performances of machine learning algorithms for binary classification of network anomaly detection system
Published 2018“…Moreover, network anomaly detection using machine learning faced difficulty when dealing the involvement of dataset where the number of labelled network dataset is very few in public and this caused many researchers keep used the most commonly network dataset (KDDCup99) which is not relevant to employ the machine learning (ML) algorithms for a classification. …”
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15
Comparative study of machine learning algorithms in data classification
Published 2025“…This research conducts a comparative study of various machine learning algorithms for dataset classification to identify the most accurate and reliable classifier. …”
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Evaluation of the Transfer Learning Models in Wafer Defects Classification
Published 2022“…Transfer Learning is one of the common methods. Various algorithms under Transfer Learning had been developed for different applications. …”
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17
Developing a hybrid model for accurate short-term water demand prediction under extreme weather conditions: a case study in Melbourne, Australia
Published 2024“…Principle component analysis was used to determine which predictors were most reliable. Hybrid model development included the optimization of ANN coefficients (its weights and biases) using adaptive guided differential evolution algorithm. …”
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Multi-Class Multi-Level Classification of Mental Health Disorders Based on Textual Data from Social Media
Published 2024“…The Multi-Class Multi-Level (MCML) classification algorithm was applied to perform detailed classification and address the limitations of the research scope using several approaches, including machine learning, deep learning, and transfer learning approaches. …”
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Differential evolution for neural networks learning enhancement
Published 2008“…Three programs have developed; Differential Evolution Neural Network (DENN), Genetic Algorithm Neural Network (GANN) and Particle Swarm Optimization with Neural Network (PSONN) to probe the impact of these methods on ANN learning using various datasets. …”
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20
An improved algorithm for iris classification by using support vector machine and binary random machine learning
Published 2018“…In machine learning, there are three type of learning branch that can used in classification procedures for data mining. …”
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