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Opposition-Based Learning Binary Bat Algorithm as Feature Selection Approach in Taguchi's T-Method by Marlan Z.M., Jamaludin K.R., Harudin N.

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Optimization of attribute selection model using bio-inspired algorithms by Basir, Mohammad Aizat, Yusof, Yuhanis, Hussin, Mohamed Saifullah

“…Attribute selection which is also known as feature selection is an essential process that is relevant to predictive analysis.To date, various feature selection algorithms have been introduced, nevertheless they all work independently. …”
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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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Ideal combination feature selection model for classification problem based on bio-inspired approach by Basir, Mohammad Aizat, Hussin, Mohamed Saifullah, Yusof, Yuhanis

“…The important step is to idealize the combined feature selection models by finding the best combination of search method and feature selection algorithms. …”
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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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Feature Selection with Harmony Search for Classification: A Review by Norfadzlan, Yusup, Azlan, Mohd Zain, Nur Fatin Liyana, Mohd Rosely, Suhaila Mohamad, Yusuf

“…From the review, feature selection with HS algorithm shows a good performance as compared to other metaheuristics algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).…”
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Enhancing Wearable-Based Human Activity Recognition with Binary Nature-Inspired Optimization Algorithms for Feature Selection by Norfadzlan, Yusup, Izzatul Nabila, Sarbini, Dayang Nurfatimah, Awang Iskandar, Azlan, Mohd Zain, Didik Dwi, Prasetya

“…Conversely, for the PAMAP2 dataset, BDE algorithm displays superior feature selection quality and BPSO algorithm maintains competitive performance and adaptability. …”
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Automated model selection for corporation credit risk assessment using machine learning / Zulkifli Halim by Halim, Zulkifli

“…As a result, the proposed automated model selection has found that the LGADS model on multi-dimensional data of FR-only features and without a features correlation setup has outperformed the other models with the highest accuracy and Fl score. …”
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Feature selection algorithms for Malaysian dengue outbreak detection model by Husam I.S. Abuhamad, Azuraliza Abu Bakar, Suhaila Zainudin, Mazura Sahani, Zainudin Mohd Ali

“…This research aimed to identify the best features that lead to better predictive accuracy of dengue outbreaks using three different feature selection algorithms; particle swarm optimization (PSO), genetic algorithm (GA) and rank search (RS). …”
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Evaluation of data mining models for predicting concrete strength by Wong, Chuan Ming

“…These models are all evaluated with hyperparameter tuning and different feature selection techniques. …”
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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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Selection and optimization of peak features for event-related eeg signals classification / Asrul bin Adam by Asrul, Adam

“…However, the developed algorithms only consider the selected features from a peak model based on the understanding of the EEG signals characteristics. …”
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Performance analysis of feature selection algorithm for educational data mining by Zaffar, M., Hashmani, M.A., Savita, K.S.

“…This paper present an analysis of the performance of feature selection algorithms on student data set. …”
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Feature optimization with metaheuristics for Artificial Neural Network-based chiller power prediction by Nor Farizan, Zakaria, Mohd Herwan, Sulaiman, Zuriani, Mustaffa

“…This research contributes a novel hybrid model, identifies key features for chiller power prediction, and establishes a benchmark for evaluating feature selection algorithms in building energy applications.…”
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Zebra optimization algorithm for feature selection by Faizan, Muhammad, Muhammad Arif, Mohamad

“…Feature selection is very important part in increasing the performance of machine learning models. …”
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A novel hybrid classification model of genetic algorithms, modified k-Nearest Neighbor and developed backpropagation neural network by Salari, Nader, Shohaimi, Shamarina, Najafi, Farid, Nallappan, Meenakshii, Karishnarajah, Isthrinayagy

“…In the present study, the results of the implementation of a novel hybrid feature selection-classification model using the above mentioned methods are presented. …”
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A study of feature selection algorithms for predicting students academic performance by Zaffar, M., Savita, K.S., Hashmani, M.A., Rizvi, S.S.H.

“…In the light of this mentioned fact, it is necessary to choose a feature selection algorithm carefully. This paper presents an analysis of the performance of filter feature selection algorithms and classification algorithms on two different student datasets. …”
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A study of feature selection algorithms for predicting students academic performance by Zaffar, M., Savita, K.S., Hashmani, M.A., Rizvi, S.S.H.

“…In the light of this mentioned fact, it is necessary to choose a feature selection algorithm carefully. This paper presents an analysis of the performance of filter feature selection algorithms and classification algorithms on two different student datasets. …”
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K-gen phishguard: an ensemble approach for phishing detection with k-means and genetic algorithm by Al-Hafiz, Ali Raheem, Jabir, Adnan J., Subramaniam, Shamala

“…This research presents a two-phase phishing detection system by employing unsupervised feature selection and supervised classification. In the first phase, the best set of features is identified by the Genetic algorithm and is utilised by the K-means clustering algorithm to divide the dataset into groups with similar traits. …”
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Optimised content-social based features for fake news detection in social media using text clustering approach by Yahya, Adnan Hussein Ali

“…In addition, this thesis tackles the feature selection problem by designing a novel wrapper feature selection method based on the Hybrid Flower Pollination Algorithm (HFPA). …”
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