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1
Formulating new enhanced pattern classification algorithms based on ACO-SVM
Published 2013“…This paper presents two algorithms that integrate new Ant Colony Optimization (ACO) variants which are Incremental Continuous Ant Colony Optimization (IACOR) and Incremental Mixed Variable Ant Colony Optimization (IACOMV) with Support Vector Machine (SVM) to enhance the performance of SVM.The first algorithm aims to solve SVM model selection problem. …”
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2
Rao-SVM machine learning algorithm for intrusion detection system
Published 2020“…Most of the intrusion detection systems are developed based on optimization algorithms as a result of the increase in audit data features; optimization algorithms are also considered for IDS due to the decline in the performance of the human-based methods in terms of their training time and classification accuracy. …”
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3
Improved hybrid teaching learning based optimization-jaya and support vector machine for intrusion detection systems
Published 2022“…To achieve this goal, an improved Teaching Learning-Based Optimization (ITLBO) algorithm was proposed in dealing with subset feature selection. …”
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4
Improved intrusion detection algorithm based on TLBO and GA algorithms
Published 2021“…The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). …”
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Comparison of Logistic Regression, Random Forest, SVM, KNN Algorithm for Water Quality Classification Based on Contaminant Parameters
Published 2024“…This research provides new insights into the application of machine learning algorithms for water quality management as well as guidance for optimal algorithm selection.…”
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A modified weighted support vector machine (WSVM) to reduce noise data in classification problem
Published 2021“…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“…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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Improvement on rooftop classification of worldview-3 imagery using object-based image analysis
Published 2019“…The accuracy of each algorithm was evaluated using LibSVM, Bayes network, and Adaboost classifier. …”
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10
A new classifier based on combination of genetic programming and support vector machine in solving imbalanced classification problem
Published 2016“…The main keys of the new classifier are based on the new kernel method, new learning metric and a new optimization algorithm in order to optimize the SVM decision function. …”
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11
An ensemble learning method for spam email detection system based on metaheuristic algorithms
Published 2015“…In the second phase, a classifier ensemble learning model is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on Binary Quantum Gravitational Search Algorithm (QBGSA), (ii) To mine data streams using various data chunks and overcome a failure of single classifiers based on SVM, MLP and K-NN algorithms. …”
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12
Efficient gear fault feature selection based on moth‑flame optimisation in discrete wavelet packet analysis domain
Published 2019“…Lastly, the MFO-selected features were used as the input for a support vector machine (SVM) diagnostic model to identify fault patterns. …”
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Activity recognition using optimized reduced kernel extreme learning machine (OPT-RKELM) / Yang Dong Rui
Published 2019“…It applies the characteristic of ReliefF algorithm to rank and select top scoring features for feature selection. …”
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14
P300 detection of brain signals using a combination of wavelet transform techniques
Published 2012“…In this research the BCI competition data-set has been processed through 5 optimized detection methods. Wavelet transform (WT), student’s two-sample t-statistic (T-Test) and support vector machines (SVM) used in designing the algorithms. …”
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15
Optimized techniques for landslide detection and characteristics using LiDAR data
Published 2018“…Also, six techniques: Ant Colony Optimization (ACO), Gain Ratio (GR), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), Random forest (RF), and Correlation-based Feature Selection (CFS) were used for the feature selection. …”
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Short Text Classification Using An Enhanced Term Weighting Scheme And Filter-Wrapper Feature Selection
Published 2018“…In the second stage, grey wolf optimization (GWO) algorithm, a new heuristic search algorithm, uses the SVM accuracy as a fitness function to find the optimal subset feature.…”
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18
Kinect-based human gait recognition using locally linear embedded and support vector machine
Published 2018“…Firstly, dynamic gait features for one gait cycle are extracted from each subject’s walking gait that is acquired using Kinect sensor. Next, the extracted gait features were then optimized using LLE known as DG-LLE and further classified by multi-class SVM with Error Correcting Output Code (ECOC) algorithm. …”
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Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: a preliminary study
Published 2024“…Methods: ML algorithms logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) models were applied. …”
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Classification of labour pain using electroencephalogram signal based on wavelet method / Sai Chong Yeh
Published 2020“…Supervised and unsupervised machine learning algorithms particularly the Support Vector Machine (SVM) and Density Based Spatial Clustering of Application with Noise (DBSCAN) are used in this study. …”
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