Search Results - (( subset selection model algorithm ) OR ( wave optimization method algorithm ))*

Formulating new enhanced pattern classification algorithms based on ACO-SVM by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…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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Mixed variable ant colony optimization technique for feature subset selection and model selection by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

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Feature selection and model selection algorithm using incremental mixed variable ant colony optimization for support vector machine classifier by Alwan, Hiba Basim, Ku-Mahamud, Ku Ruhana

“…Support Vector Machine (SVM) is a present day classification approach originated from statistical approaches.Two main problems that influence the performance of SVM are selecting feature subset and SVM model selection. In order to enhance SVM performance, these problems must be solved simultaneously because error produced from the feature subset selection phase will affect the values of the SVM parameters and resulted in low classification accuracy.Most approaches related with solving SVM model selection problem will discretize the continuous value of SVM parameters which will influence its performance.Incremental Mixed Variable Ant Colony Optimization (IACOMV) has the ability to solve SVM model selection problem without discretising the continuous values and simultaneously solve the two problems.This paper presents an algorithm that integrates IACOMV and SVM.Ten datasets from UCI were used to evaluate the performance of the proposed algorithm.Results showed that the proposed algorithm can enhance the classification accuracy with small number of features.…”
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An ensemble learning method for spam email detection system based on metaheuristic algorithms by Behjat, Amir Rajabi

“…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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Short-term electricity price forecasting in deregulated electricity market based on enhanced artificial intelligence techniques / Alireza Pourdaryaei by Alireza , Pourdaryaei

“…The proposed feature selection technique comprises of Multi-objective Binary-valued Backtracking Search Algorithm (MOBBSA). …”
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Inversion Of Surface Wave Phase Velocity Using New Genetic Algorithm Technique For Geotechnical Site Investigation by Hamimu, La

“…Therefore the use of genetic algorithm (GA) optimization technique which is one of nonlinear optimization methods is an appropriate choice to solve surface wave inversion problem having high nonlinearity and multimodality. …”
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Modelling the yield loss of oil palm due to Ganoderma Basal Stem Rot disease by Assis Kamu

“…For estimation-post-selection approach, there were two subset selection algorithms were applied, namely backward stepwise subset selection and best-subset selection. …”
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Modelling the yield loss of oil palm due to ganoderma basal stem rot disease by Assis bin Kamu

“…For estimation-post-selection approach, there were two subset selection algorithms were applied, namely backward stepwise subset selection and best-subset selection. …”
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Modelling of optimized hybrid debris flow using airborne laser scanning data in Malaysia by Lay, Usman Salihu

“…Cuckoo search), and evaluator or model inducing algorithms (e.g SVM) were utilized for feature subset selection, which further compared to select the optimal conditioning factors subset. …”
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Optimizing the efficiency of Oscillating Water Column (OWC) wave energy converter using genetic algorithm by Nallagownden, P., Alhaj, H.M.M., Sarwar, M.B.

“…This paper, describes a method to maximize the pneumatic system efficiency using optimization technique based on Genetic algorithm. …”
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Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi by Atefi, Kayvan

“…Experiments demonstrate and prove that the proposed EBPSO method produces better accuracy mining data and selecting subset of relevant features comparing other algorithms. …”
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A model for gene selection and classification of gene expression data by Mohamad, Mohd Saberi, Omatu, Sigeru, Deris, Safaai, Mohd Hashim, Siti Zaiton

“…A model for gene selection and classification has been developed by using a filter approach, and an improved hybrid of the genetic algorithm and a support vector machine classifier. …”
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Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G by Salh, Adeb, Audah, Lukman, Abdullah, Qazwan, Aydoğdu, Ömer, A. Alhartomi, Mohammed, Alsamhi, Saeed Hamood, A. Almalki, Faris, M. Shah, Nor Shahida

“…We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). …”
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Low computational complexity for optimizing energy efficiency in mm-wave hybrid precoding system for 5G by Salh, Adeb, Audah, Lukman, Abdullah, Qazwan, Aydoğdu, Ömer, A. Alhartomi, Mohammed, Alsamhi, Saeed Hamood, A. Almalki, Faris, M. Shah, Nor Shahida

“…We propose effective alternating minimization algorithms based on the zero gradient method to establish fully-connected structures (FCSs) and partially-connected structures (PCSs). …”
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Correlation-based subset evaluation of feature selection for dynamic Malaysian sign language by Sutarman, .

“…The experiments have achieved 95.56 % in accuration rates for Correlation-based Feature Subset Evaluation (CfsSubsetEval).…”
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Robust variable selection methods for large- scale data in the presence of multicollinearity, autocorrelated errors and outliers by Uraibi, Hassan S.

“…The BAE-Net is found to do a credible job in selecting the correct important variables in the final model.…”
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SVM for network anomaly detection using ACO feature subset by Mehmood, T., Rais, H.B.M.

“…But irrelevant and redundant features are the obstacle for classification algorithm to build an efficient detection model. This paper proposes a detection model, ant system with support vector machine, which uses ant system, a variation of ant colony optimization, to filter out the redundant and irrelevant features for support vector machine classification algorithm. …”
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Metaheuristic algorithms for feature selection (2014–2024) by Faizan, Muhammad, Muhammad Arif, Mohamad

“…Feature selection is a process used during machine learning and data analysis, aimed at selecting the best features to increase model efficiency, decrease complexity, and increase readability. …”
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Two-stage feature selection using ranking self-adaptive differential evolution algorithm for recognition of acceleration activity by Zainudin, Muhammad Noorazlan Shah, Sulaiman, Md. Nasir, Mustapha, Norwati, Perumal, Thinagaran, Mohamed, Raihani

“…Consequently, this paper proposes a ranking self-adaptive differential evolution (rsaDE) feature selection algorithm. The proposed algorithm is capable of selecting the optimal feature subsets while improving the recognition of acceleration activity using a minimum number of features. …”
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Heuristic optimization-based wave kernel descriptor for deformable 3D shape matching and retrieval by Naffouti, S.E., Fougerolle, Y., Aouissaoui, I., Sakly, A., Mériaudeau, F.

“…This paper presents an optimized wave kernel signature (OWKS) using a modified particle swarm optimization (MPSO) algorithm. …”
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