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Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…The comparison showed that, the accuracy of the unsupervised classification map with value of 88.4% that was generated by using the cluster labelling algorithm was slightly more than the maximum-likelihood supervised classification map with value of 87.5%. …”
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Enhancing Classification Algorithms with Metaheuristic Technique
Published 2024“…Implementing this process uses classification algorithms such asNaïve Bayes, Support Vector Machine,and Random Forest. …”
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Aco-based feature selection algorithm for classification
Published 2022“…The modified graph clustering ant colony optimisation (MGCACO) algorithm is an effective FS method that was developed based on grouping the highly correlated features. …”
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Realization Of The 1D Local Binary Pattern (LBP) Algorithm In Raspberry Pi For Iris Classification Using K-NN Classifier
Published 2018“…There are a lot of feature extraction methods and classification methods for iris classification. …”
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Evaluation and Comparative Analysis of Feature Extraction Methods on Image Data to increase the Accuracy of Classification Algorithms
Published 2024“…The classification algorithm used in this research is the Convolutional Neural Network (CNN) algorithm. …”
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Feature extraction and selection algorithm based on self adaptive ant colony system for sky image classification
Published 2023“…The performance of FESSIC was evaluated against ten benchmark image classification algorithms and six classifiers on four ground-based sky image datasets. …”
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Hybrib NSGA-II optimization for improving the three-term BP network for multiclass classification problems
Published 2015“…This paper presents a hybrid of the multiobjective evolutionary algorithm to gain a better accuracy of the fi nal solutions.The aim of using the hybrid algorithm is to improve the multiobjective evolutionary algorithm performance in terms of the enhancement of all the individuals in the population and increase the quality of the Pareto optimal solutions.The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. …”
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Hybrid NSGA-II Optimization for Improving the Three-Term BP Network for Multiclass Classification Problems
Published 2015“…The multiobjective evolutionary algorithm used in this study is a nondominated sorting genetic algorithm-II (NSGA-II) together with its hybrid, the backpropagation algorithm (BP), which is used as a local search algorithm to optimize the accuracy and complexity of the three-term backpropagation (TBP) network. …”
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Jogging activity recognition using k-NN algorithm
Published 2022“…Jogging activity recognition using the k-NN algorithm is a system that can help users collect information data of user speed movement using speed sensor and give the classification of jogging activity to the user. …”
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Static code analysis of permission-based features for android malware classification using apriori algorithm with particle swarm optimization
Published 2015“…However, supervised learning technique has limitations for malware classification task. This paper presents a classification approach on android malware using candidate detectors generated from an unsupervised association rule of Apriori Algorithm. …”
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Mutable Composite Firefly Algorithm for Microarray-Based Cancer Classification
Published 2024“…Recently, the swarm-based hybrid algorithms have given significant performance in cancer classification. …”
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A Data Mining Approach to Construct Graduates Employability Model in Malaysia
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Arabic text classification using hybrid feature selection method using chi-square binary artificial bee colony algorithm
Published 2021“…After that, the wrapper method, Artificial Bee Colony algorithm, is used as the second level where Naive Base is used as a fitness function. …”
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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 of JPEG files by using extreme learning machine
Published 2018“…The algorithm automatically classifies the files based on evaluation measures of three methods Entropy, Byte Frequency Distribution and Rate of Change. …”
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Computational Technique for an Efficient Classification of Protein Sequences With Distance-Based Sequence Encoding Algorithm
Published 2017“…In this article, we have proposed a distance-based sequence encoding algorithm that captures the sequence's statistical characteristics along with amino acids sequence order information. …”
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Document classification based on kNN algorithm by term vector space reduction
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A hybrid approach for artificial immune recognition system / Mahmoud Reza Saybani
Published 2016“…The increasing size of data being stored have created the need for computer-based methods for automatic data analysis. Many researchers, who have developed methods and algorithms within the field of artificial intelligence, machine learning and data mining, have addressed extracting useful information from the data. …”
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Fuzzy C-Mean And Genetic Algorithms Based Scheduling For Independent Jobs In Computational Grid
Published 2006“…Our model presents the method of the jobs classifications based mainly on Fuzzy C-Mean algorithm and mapping the jobs to the appropriate resources based mainly on Genetic algorithm. …”
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Extremal region detection and selection with fuzzy encoding for food recognition
Published 2019“…The performance of algorithms was measured based on classification accuracy, error rate, and precision and recall. …”
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