Search Results - (( based optimization based algorithm ) OR ( using classification clustering algorithm ))
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Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering
Published 2022“…Moreover, interpretability also recorded better results on testing problems, where most of the number of rules were fewer than 33. A clustering algorithm based on MOPSO-CD with a modified archive update mechanism (MCPSO-CD) was used to estimate the optimal number of clusters. …”
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An ensemble data summarization approach based on feature transformation to learning relational data
Published 2015“…A ensemble clustering is designed, used and evaluated to generate the final classification framework that will take all input generated from the GA based clustering with Feature Selection and Feature Construction algorithms and perform the classification task for the relational datasets. …”
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Application of Optimization Methods for Solving Clustering and Classification Problems
Published 2011“…The data vectors are assigned to the closest cluster and correspondingly to the set, which contains this cluster and an algorithm based on a derivative-free method is applied to the solution of this problem. …”
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4
Balancing Exploitation And Exploration Search Behavior On Nature-Inspired Clustering Algorithms
Published 2018“…Nature-inspired optimization-based clustering techniques are powerful, robust and more sophisticated than the conventional clustering methods due to their stochastic and heuristic characteristics. …”
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5
Efficient classifying and indexing for large iris database based on enhanced clustering method
Published 2018“…In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…Then, the selected features are given as input to the DBN classifier which is trained using the Taylor-based bird swarm algorithm (Taylor-BSA). …”
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Hyper-heuristic framework for sequential semi-supervised classification based on core clustering
Published 2020“…This article presents a prominent framework that integrates each of the NN, a meta-heuristic based on evolutionary genetic algorithm (GA) and a core online-offline clustering (Core). …”
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Whale optimization algorithm based on tent chaotic map for feature selection in soft sensors
Published 2025“…The soft sensor was designed using several stages, including data collection, preprocessing, clustering, feature selection, and classification. …”
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Development of lung cancer prediction system using meta-heuristic optimized deep learning model
Published 2023“…After that cancer-affected region in the lung is segmented with the help of the proposed Butterfly Optimization Algorithm-based K-Means Clustering (BOAKMC) algorithm. …”
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Detecting lung cancer region from CT image using meta-heuristic optimized segmentation approach
Published 2022“…In this paper, the butterfly optimization algorithm-based K-means clustering (BOAKMC) method is introduced for reducing CT image segmentation uncertainty. …”
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Reliable multiclass cancer classification of microarray gene expression profiles using an improved wavelet neural network
Published 2011“…Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MSFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…When noise exists in training data, the decision boundary of SVM would deviate from the optimal hyperplane severely. To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…When noise exists in training data, the decision boundary of SVM would deviate from the optimal hyperplane severely. To overcome SVM drawback for noise data problem, WSVM using KPCM algorithm was used but WSVM using kernel-based learning algorithm such as KPCM algorithm suffer from training complexity, expensive computation time and storage memory when noise data contaminate training data. …”
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An Improved Wavelet Neural Network For Classification And Function Approximation
Published 2011“…Second, the proposed enhanced fuzzy c-means clustering algorithm—specifically, the modified point symmetry-based fuzzy c-means (MPSDFCM) algorithm—was employed in selecting the locations of the translation vectors of the WNN. …”
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Plant identification using combination of fuzzy c-means spatial pyramid matching, gist, multi-texton histogram and multiview dictionary learning
Published 2016“…Beside that, classic bag of visual words algorithm (BoVW) is based on kmeans clustering and every SIFT feature belongs to one cluster and it leads to decreasing classification results. …”
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An application of a novel technique for assessing the operating performance of existing cooling systems on a university campus
Published 2018“…Two contributions in this work are: (1) the prediction of a model by using Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Fuzzy Clustering Subtractive (FCS), and (2) the classification and optimization of the predicted models by using an Accelerated Particle Swarm Optimization (APSO) algorithm. …”
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Statistical data preprocessing methods in distance functions to enhance k-means clustering algorithm
Published 2018“…The K-Means algorithm is the commonest and fast technique in partitional cluster algorithms, although with unnormalized datasets it can achieve local optimal. …”
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Spatial Clustering Algorithm for Time Series Rainfall Data Using X-Means Data Splitting
Published 2017“…The performance of the clustering algorithm gets even worse if it relies on actual data and many clustering algorithms are often faced with conflict in handling high dimensional data. …”
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