Search Results - (( based optimization based algorithm ) OR ( level classification problems algorithm ))
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1
A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…There are two methods in dealing with imbalanced classification problem, which are based on data or algorithmic level. …”
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2
Software defect prediction framework based on hybrid metaheuristic optimization methods
Published 2015“…For the purpose of this study, ten classification algorithms have been selected. The selection aims at achieving a balance between established classification algorithms used in software defect prediction. …”
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3
Fair bandwidth distribution marking and scheduling algorithm in network traffic classification
Published 2019“…Second, an Optimized time sliding window packet marker (OTSWTCM) algorithm. …”
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4
Taylor-Bird Swarm Optimization-Based Deep Belief Network For Medical Data Classification
Published 2022“…However, finding the most appropriate deep learning algorithm for a medical classification problem along with its optimal parameters becomes a difficult task. …”
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5
Predicting building damage grade by earthquake: a Bayesian Optimization-based comparative study of machine learning algorithms
Published 2024“…The class imbalance makes estimating building damage grades difficult, emphasizing the necessity for careful modeling. Bayesian Optimization optimizes machine learning algorithm hyperparameters to solve this problem. …”
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6
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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7
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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8
An optimal mesh algorithm for remote protein homology detection
Published 2011“…Remote protein homology detection is a problem of detecting evolutionary relationship between proteins at low sequence similarity level. …”
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9
Analyzing CT images for detecting lung cancer by applying the computational intelligence-based optimization techniques
Published 2022“…The gathered image noise is removed by applying the mean filter, and the affected regions are segmented with the help of the Butterfly Optimization Algorithm-based K-Means Clustering (BOAKMC)algorithm. …”
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10
Component-wise analysis of metaheuristic algorithms for novel fuzzy-meta classifier
Published 2018“…The proposed improved PSO (iPSO), improved ABC (iABC), and improved CS (iCS) outperformed standard algorithms and variants from existing literature, as well as, Grey Wolf Optimization (GWO) and Animal Migration Optimization (AMO) on ten numerical optimization problems with varying modalities. …”
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11
Multilingual Sentiment Analysis From Students Feedback: Optimal Techniques And Resources For Bengali Lingual Data
Published 2024thesis::doctoral thesis -
12
Integration of GWO-LSSVM for time series predictive analysis
Published 2016“…The emergence of Statistical Learning Theory (SLT) based algorithm namely Least Squares Support Vector Machines (LSSVM) has evidenced its efficacy in solving regression and classification problems. …”
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13
Adaptive multi-parent crossover GA for feature optimization in epileptic seizure identification
Published 2019“…The GA-based approach is used to optimize the various features obtained. …”
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14
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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15
Adaptive differential evolution algorithm with fitness based selection of parameters and mutation strategies / Rawaa Dawoud Hassan Al-Dabbagh
Published 2015“…DE has effectively solved various global optimization problems, including benchmark functions. …”
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16
Detection of corneal arcus using rubber sheet and machine learning methods
Published 2019“…The benchmark of the classification algorithm for CA is needed to analyze the optimal output of the algorithm. …”
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17
An efficient anomaly intrusion detection method with evolutionary neural network
Published 2020“…The second anomaly detection method is the Evolutionary Kernel Neural Network Random Weights (EKNNRW) in order to increase the accuracy of classification. The third proposed method is a new Evolutionary Neural Network (ENN) algorithm with a combination of Genetic Algorithm and Multiverse Optimizer (GAMVO) as a training part of ANN to create efficient anomaly-based detection with low false alarm rate. …”
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18
Feature selection optimization using hybrid relief-f with self-adaptive differential evolution
Published 2017“…Differential evolution (DE) is one of the evolutionary algorithms that are widely used in various classification domains. …”
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Color Image Segmentation Based on Bayesian Theorem for Mobile Robot Navigation
Published 2009“…Bayesian classification and decision making are based on probability theory and choosing the most probable or the lowest risk. …”
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20
Class binarization with self-adaptive algorithm to improve human activity recognition
Published 2018“…In order to improve recognition of high interclass similarity activities, One-Versus- All (OVA) binarization strategy is introduced by transforming original multi-class classification problems into a series of two-class classification problems. …”
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