Article
Mathematics
Ibrahim Al-Shourbaji, Na Helian, Yi Sun, Samah Alshathri, Mohamed Abd Elaziz
Summary: This paper discusses the importance of feature selection in the telecommunications industry for machine learning models. It introduces a new approach that combines ant colony optimization and reptile search algorithm, and evaluates its performance in customer churn prediction.
Article
Multidisciplinary Sciences
Zakir Hussain Ahmed, Asaad Shakir Hameed, Modhi Lafta Mutar, Habibollah Haron
Summary: The capacitated vehicle routing problem is a challenging problem widely used in transportation, logistics, and distribution. Researchers have developed heuristic/metaheuristic algorithms, such as the ant colony optimization, to solve large-sized instances within a reasonable computational time. However, existing algorithms often suffer from premature convergence and stagnation issues. In this study, an enhanced ACS algorithm based on subpaths is proposed to address these issues and improve the performance. The algorithm incorporates the K-nearest neighbour algorithm for finding the best initial solution and enhances diversity by avoiding the generation of the same solution using subpaths. Experimental results demonstrate the effectiveness of the proposed algorithm compared to the enhanced simulated annealing algorithm.
Article
Computer Science, Artificial Intelligence
Namrata Karlupia, Pawanesh Abrol
Summary: Nature-inspired computing, which mimics natural processes, provides machine solutions to complex problems. The challenge of high-dimensional data with redundant features is addressed using metaheuristic techniques, particularly the whale optimization algorithm (WOA). In this study, five nature-inspired algorithms were compared for feature selection, and WOA was found to perform the best.
NEURAL COMPUTING & APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Suvita Rani Sharma, Birmohan Singh, Manpreet Kaur
Summary: The binary versions of Rao algorithms are proposed for solving feature selection problems in Parkinson's disease datasets, optimizing the k parameter of the k-nearest neighbour classifier. The performance of these algorithms is evaluated through 30 independent runs with a 10-fold cross-validation procedure and compared with state of the art methods, with significance analysis conducted using the Friedman rank test.
Article
Computer Science, Theory & Methods
Padraig Cunningham, Sarah Jane Delany
Summary: The article provides an overview of Nearest Neighbour classification techniques, focusing on similarity assessment mechanisms, computational issues in identifying nearest neighbours, and methods for reducing the dimension of the data. New sections on similarity measures for time-series, retrieval speedup, and intrinsic dimensionality have been added, along with an Appendix containing Python code for key methods.
ACM COMPUTING SURVEYS
(2021)
Article
Computer Science, Artificial Intelligence
Guo Feng Anders Yeo, Vural Aksakalli
Summary: A novel methodology based on simultaneous perturbation stochastic approximation (SPSA) for simultaneous feature selection and weighting for nearest neighbour (NN) learners is introduced in this study. Extensive computational experiments show that SPSA-FWS generally outperforms existing feature weighting algorithms and stands as a competitive new method for this task. Additionally, SPSA-FWS has attractive features allowing it to be used with any performance metric and any variant of nearest neighbour learners, and to be hybridised with other feature weighting methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Razieh Asgarnezhad, S. Amirhassan Monadjemi, Mohammadreza Soltanaghaei
Summary: The study emphasizes the importance of pre-processing and data reduction techniques in sentiment classification and proposes a new algorithm that significantly improves accuracy, precision, and recall in classification.
JOURNAL OF SUPERCOMPUTING
(2021)
Article
Computer Science, Hardware & Architecture
Huru Hasanova, Muhammad Tufail, Ui-Jun Baek, Jee-Tae Park, Myung-Sup Kim
Summary: In this article, a machine learning based Sine Cosine Weighted K-Nearest Neighbour (SCA_WKNN) algorithm is proposed for heart disease prediction, which learns from data stored in blockchain. The proposed algorithm achieves higher accuracy compared to other algorithms. Blockchain-based storage also achieves higher throughput.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Wenping Ma, Xiaobo Zhou, Hao Zhu, Longwei Li, Licheng Jiao
Summary: The paper introduces a two-stage hybrid ACO algorithm for high-dimensional feature selection, which is capable of handling large-scale datasets efficiently with shorter running time.
PATTERN RECOGNITION
(2021)
Article
Computer Science, Artificial Intelligence
Mohsen Paniri, Mohammad Bagher Dowlatshahi, Hossein Nezamabadi-pour
Summary: This paper proposes a new multi-label feature selection method based on Ant Colony Optimization, using a heuristic learning approach to enhance performance. Experimental results demonstrate that the proposed method significantly outperforms competing methods.
SWARM AND EVOLUTIONARY COMPUTATION
(2021)
Review
Computer Science, Artificial Intelligence
Esther Omolara Abiodun, Abdulatif Alabdulatif, Oludare Isaac Abiodun, Moatsum Alawida, Abdullah Alabdulatif, Rami S. Alkhawaldeh
Summary: Data preparation techniques such as feature selection are crucial for optimizing predictive models for classification tasks. Traditional feature selection methods may not effectively reduce high dimensionality in text data, but emerging technologies like metaheuristics and hyper-heuristics optimization methods offer new possibilities for improving model accuracy and efficiency. Despite the potential benefits, there is still a need for best practices in utilizing these emerging feature selection methods for text classification tasks.
NEURAL COMPUTING & APPLICATIONS
(2021)
Review
Computer Science, Information Systems
Maha Nssibi, Ghaith Manita, Ouajdi Korbaa
Summary: The main objective of feature selection is to improve learning performance by selecting concise and informative feature subsets. This paper explores nature-inspired metaheuristic methods for the feature selection problem, with a focus on representation and search algorithms. An analysis of various advanced approach types and their advantages and disadvantages is provided, along with guidance for conducting future research effectively in this field.
COMPUTER SCIENCE REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Baoshuang Zhang, Yanying Li, Zheng Chai
Summary: Feature selection is a preprocessing technology that reduces the dimension of a dataset by acquiring a subset of features with the most information and improves classification accuracy. This research proposes a novel method based on random multi-subspace for feature selection, which demonstrates its effectiveness, competitiveness, and superiority in solving feature selection problems.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Rajalaxmi Ramasamy Rajammal, Seyedali Mirjalili, Gothai Ekambaram, Natesan Palanisamy
Summary: This article introduces a wrapper-based Binary Improved Grey Wolf Optimizer (BIGWO) approach for categorizing Parkinson's disease and feature selection. The proposed method utilizes an adaptive k-nearest neighbor algorithm to optimize the feature selection process, and experimental results demonstrate its superior performance compared to other algorithms.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Savita Wadhawan, Raman Maini
Summary: Cardiac disease is a leading cause of death worldwide. The lack of radiologists and doctors is a barrier to early diagnosis. Computational intelligence can assist in diagnosing diseases and relieving the workload of medical professionals. This study proposes an effective technique for cardiac disease prediction based on machine intelligence. With accurate data generation and feature selection, the proposed technique improves the accuracy of prediction models and outperforms baseline approaches in terms of accuracy.
KNOWLEDGE-BASED SYSTEMS
(2022)
Review
Computer Science, Artificial Intelligence
Siti Rohaidah Ahmad, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub
INTELLIGENT DATA ANALYSIS
(2019)
Article
Computer Science, Hardware & Architecture
Amin Mahmoudi, Mohd Ridzwan Yaakub, Azuraliza Abu Bakar
Article
Computer Science, Artificial Intelligence
Jamilu Awwalu, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub
NEURAL COMPUTING & APPLICATIONS
(2019)
Article
Computer Science, Information Systems
Amin Mahmoudi, Mohd Ridzwan Yaakub, Azuraliza Abu Bakar
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2019)
Review
Computer Science, Information Systems
Ibrahim Said Ahmad, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub
INFORMATION PROCESSING & MANAGEMENT
(2020)
Review
Multidisciplinary Sciences
Suhaib Kh Hamed, Mohd Juzaiddin Ab Aziz, Mohd Ridzwan Yaakub
Summary: Social networks have become the main source for news consumption, but the spread of fake news on these platforms has negative consequences. Many studies have proposed effective models for detecting fake news in social networks, but their accuracy is often insufficient. Previous reviews have focused on specific aspects of fake news detection models, overlooking the impact of datasets, features, and fusion methods. This review analyzes recent studies to highlight the challenges and performance implications of fake news detection models.
Proceedings Paper
Computer Science, Cybernetics
Nurul Natasha Awinda Mohammad Nizam, Mohd Fahmi Mohamad Amran, Nurhafizah Moziyana Mohd Yusop, Siti Rohaidah Ahmad, Norshahriah Abdul Wahab
Summary: This paper examines the rapid growth of eCommerce in Malaysia, particularly during the Covid-19 pandemic, and explores how consumer behavior and decision making are influenced by emotions. It focuses on the emerging field of neuromarketing study, specifically using the electroencephalogram (EEG) technique. The paper aims to determine consumer behaviors towards marketing stimuli and how their emotions are influenced by these stimuli.
CYBERNETICS PERSPECTIVES IN SYSTEMS, VOL 3
(2022)
Article
Computer Science, Information Systems
Abdollah Ansari, Ibrahim Said Ahmad, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub
Article
Computer Science, Information Systems
Amin Mahmoudi, Azuraliza Abu Bakar, Mehdi Sookhak, Mohd Ridzwan Yaakub
Article
Education & Educational Research
Muslihah Wook, Suhaila Ismail, Nurhafizah Moziyana Mohd Yusop, Siti Rohaidah Ahmad, Arniyati Ahmad
EDUCATION AND INFORMATION TECHNOLOGIES
(2019)
Article
Computer Science, Theory & Methods
Siti Rohaidah Ahmad, Nurhafizah Moziyana Mohd Yusop, Muslihah Wook, Arniyati Ahmad, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2018)
Article
Computer Science, Theory & Methods
Ibrahim Said Ahmad, Azuraliza Abu Bakar, Mohd Ridzwan Yaakub, Mohammad Darwich
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS
(2020)