Article
Computer Science, Artificial Intelligence
Yongbin Zhu, Tao Li, Xiaolong Lan
Summary: Improving classification performance is crucial for practical applications, and feature selection is an important preprocessing step in machine learning systems. However, existing methods based on heuristic search strategies often have high running costs. This paper proposes an efficient feature selection method based on artificial immune algorithm optimization, which introduces a clone selection algorithm and genetic shuffling technology to improve search performance. Experimental results show that this method achieves better classification accuracy with fewer selected features and lower computational cost compared to other methods.
APPLIED INTELLIGENCE
(2023)
Article
Computer Science, Artificial Intelligence
Jing Wang, Yuanzi Zhang, Minglin Hong, Haiyang He, Shiguo Huang
Summary: This paper proposes a self-adaptive level-based learning artificial bee colony (SLLABC) algorithm for high-dimensional feature selection problem. The algorithm introduces novel mechanisms to accelerate convergence, balance exploration and exploitation abilities, and reduce the number of selected features. Experimental results show that the proposed SLLABC algorithm achieves competitive performance in terms of classification accuracy and feature subset size.
Article
Computer Science, Artificial Intelligence
Yongbin Zhu, Wenshan Li, Tao Li
Summary: This paper proposes a hybrid feature selection method based on artificial immune algorithm optimization (HFSIA) to solve the feature reduction problem of high-dimensional data. Experimental comparisons show that HFSIA has comparable computational cost to classical feature selection methods known for their speed, while achieving higher average classification accuracy and feature reduction rate on benchmark datasets.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Chemistry, Analytical
Sunil Kumar Prabhakar, Harikumar Rajaguru, Semin Ryu, In Cheol Jeong, Dong-Ok Won
Summary: Manual sleep stage scoring is a hectic task, leading to the development of automated sleep stage classification systems. This study proposes a holistic strategy combining clustering, dimensionality reduction, feature extraction and selection, and deep learning for sleep stage classification. The methodology surpasses previous studies in terms of classification accuracy, reporting a high accuracy of 93.51% even for a six-class classification problem.
Article
Computer Science, Artificial Intelligence
Qurrat Ul Ain, Harith Al-Sahaf, Bing Xue, Mengjie Zhang
Summary: This study analyzes GP-based approaches to skin image classification, which improve the performance of machine learning classification algorithms by constructing features, thereby enhancing diagnostic efficiency and assisting dermatologists in diagnosis.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Dalal Al-Alimi, Zhihua Cai, Mohammed A. A. Al-Qaness, Abdelghani Dahou, Eman Ahmed Alawamy, Sakinatu Issaka
Summary: In hyperspectral image processing, dimensionality reduction methods are crucial for reducing complexity and improving classification accuracy. The newly introduced Compression and Reinforced Variation (CRV) method shows promising results in reducing data dimension while improving classification accuracy.
APPLIED SOFT COMPUTING
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Yi Wang, Tao Li, Xiaojie Liu, Jian Yao
Summary: In this study, an efficient discrete clonal selection algorithm (DCSA-LFS) for local feature selection is proposed. It selects a distinct optimized feature subset for each different sample region by considering local sample behaviors and using a local clustering-based evaluation criterion. The algorithm enhances the search capability by using a differential evolution-based mutation operator and adopts a two-part antibody representation to automatically adjust parameters.
2022 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC)
(2022)
Review
Computer Science, Artificial Intelligence
Julliano Trindade Pintas, Leandro A. F. Fernandes, Ana Cristina Bicharra Garcia
Summary: The systematic literature review (SLR) assessed 1376 unique papers on feature selection methods in text classification published in the past eight years. Through mapping different aspects of proposed methods and identifying main characteristics of experiments, the SLR helps researchers develop new studies and position them in the context of existing literature.
ARTIFICIAL INTELLIGENCE REVIEW
(2021)
Article
Computer Science, Artificial Intelligence
Laura Moran-Fernandez, Veronica Bolon-Canedo
Summary: In this study, the performance of various feature selection approaches are compared to random selection to determine the most effective strategy. The findings indicate that correlation-based feature selection is the most effective strategy regardless of the scenario.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Sanjoy Chakraborty, Apu Kumar Saha, Absalom E. Ezugwu, Ratul Chakraborty, Ashim Saha
Summary: The study presents an enhanced version of the Whale Optimization Algorithm (WOA) called HCCWOA, which incorporates horizontal crossover, co-operative learning techniques, and inertia weight to improve the exploration and exploitation capabilities. The effectiveness of HCCWOA is evaluated on twelve datasets and compared with other algorithms, demonstrating its superior performance. Statistical analyses further support the efficacy of HCCWOA in effectively exploring feature spaces and selecting relevant characteristics for classification tasks.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Essam H. Houssein, Diego Oliva, Emre Celik, Marwa M. Emam, Rania M. Ghoniem
Summary: Feature selection is an optimization problem that aims to simplify and improve the quality of highly dimensional datasets by selecting prominent features and eliminating redundant and irrelevant data to enhance classification accuracy. The Sooty Tern Optimization Algorithm (STOA) and its improved version mSTOA are used to optimize the feature selection problem. However, mSTOA performs better than STOA in terms of convergence to optimal solutions, as validated through experiments and statistical analyses.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Dalal AL-Alimi, Mohammed A. Al-qaness, Zhihua Cai, Eman Ahmed Alawamy
Summary: This study introduces a novel feature reduction method called improving distribution analysis (IDA) to enhance data distribution, reduce complexity, and accelerate performance in hyperspectral images (HSIs). The experimental results demonstrate that IDA performs admirably in achieving these goals.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Heba Mamdouh Farghaly, Tarek Abd El-Hafeez
Summary: The feature selection problem is a significant challenge in pattern recognition, especially for classification tasks. This work explores the use of association analysis in data mining to select meaningful features and proposes a novel feature selection technique for text classification. The technique effectively reduces redundant information while achieving high accuracy using only 6% of the features.
Article
Computer Science, Artificial Intelligence
Behrouz Samieiyan, Poorya MohammadiNasab, Mostafa Abbas Mollaei, Fahimeh Hajizadeh, Mohammadreza Kangavari
Summary: Feature selection techniques are crucial for simplifying problems, improving performance, and optimizing computational efficiency while ensuring interpretability. This study presents a novel feature selection algorithm based on the crow search algorithm, which optimizes the balance between global and local search processes and achieves significant feature reduction.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Mathematical & Computational Biology
Qi Mao, Shuguang Zhao, Lijia Ren, Zhiwei Li, Dongbing Tong, Xing Yuan, Haibo Li
Summary: A novel approach using intelligent immune clonal selection and classification algorithm for pulmonary nodule CAD system was proposed and developed in this study. Experimental results showed that the proposed method had high accuracy and low FPR.
MATHEMATICAL BIOSCIENCES AND ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Jin Zhang, Zekang Bian, Shitong Wang
Summary: This study proposes a novel style linear k-nearest neighbor method to extract stylistic features using matrix expressions and improve the generalizability of the predictor through style membership vectors.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qifeng Wan, Xuanhua Xu, Jing Han
Summary: In this study, we propose an innovative approach for dimensionality reduction in large-scale group decision-making scenarios that targets linguistic preferences. The method combines TF-IDF feature similarity and information loss entropy to address challenges in decision-making with a large number of decision makers.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Hegui Zhu, Yuchen Ren, Chong Liu, Xiaoyan Sui, Libo Zhang
Summary: This paper proposes an adversarial attack method based on frequency information, which optimizes the imperceptibility and transferability of adversarial examples in white-box and black-box scenarios respectively. Experimental results validate the superiority of the proposed method and its application in real-world online model evaluation reveals their vulnerability.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jing Tang, Xinwang Liu, Weizhong Wang
Summary: This paper proposes a hybrid generalized TODIM approach in the Fine-Kinney framework to evaluate occupational health and safety hazards. The approach integrates CRP, dynamic SIN, and PLTSs to handle opinion interactions and incomplete opinions among decision makers. The efficiency and rationality of the proposed approach are demonstrated through a numerical example, comparison, and sensitivity studies.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Shigen Shen, Chenpeng Cai, Zhenwei Li, Yizhou Shen, Guowen Wu, Shui Yu
Summary: To address the damage caused by zero-day attacks on SIoT systems, researchers propose a heuristic learning intrusion detection system named DQN-HIDS. By integrating Deep Q-Networks (DQN) into the system, DQN-HIDS gradually improves its ability to identify malicious traffic and reduces resource workloads. Experiments demonstrate the superior performance of DQN-HIDS in terms of workload, delayed sample queue, rewards, and classifier accuracy.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Song Deng, Qianliang Li, Renjie Dai, Siming Wei, Di Wu, Yi He, Xindong Wu
Summary: In this paper, we propose a Chinese text classification algorithm based on deep active learning for the power system, which addresses the challenge of specialized text classification. By applying a hierarchical confidence strategy, our model achieves higher classification accuracy with fewer labeled training data.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Kaan Deveci, Onder Guler
Summary: This study proves the lack of robustness in nonlinear IF distance functions for ranking intuitionistic fuzzy sets (IFS) and proposes an alternative ranking method based on hypervolume metric. Additionally, the suggested method is extended as a new multi-criteria decision making method called HEART, which is applied to evaluate Turkey's energy alternatives.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Fu-Wing Yu, Wai-Tung Ho, Chak-Fung Jeff Wong
Summary: This research aims to enhance the energy management in commercial building air-conditioning systems, specifically focusing on chillers. Ridge regression is found to outperform lasso and elastic net regression when optimized with the appropriate hyperparameter, making it the most suitable method for modeling the system coefficient of performance (SCOP). The key variables that strongly influence SCOP include part load ratios, the operating numbers of chillers and pumps, and the temperatures of chilled water and condenser water. Additionally, July is identified as the month with the highest potential for performance improvement. This study introduces a novel approach that balances feature selection, model accuracy, and optimal tuning of hyperparameters, highlighting the significance of a generic and simplified chiller system model in evaluating energy management opportunities for sustainable operation. The findings from this research can guide future efforts towards more energy-efficient and sustainable operations in commercial buildings.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Xiaoyan Chen, Yilin Sun, Qiuju Zhang, Xuesong Dai, Shen Tian, Yongxin Guo
Summary: In this study, a method for dynamically non-destructive grasping of thin-skinned fruits is proposed. It utilizes a multi-modal depth fusion convolutional neural network for image processing and segmentation, and combines the evaluation mechanism of optimal grasping stability and the forward-looking non-destructive grasp control algorithm. The proposed method greatly improves the comprehensive performance of grasping delicate fruits using flexible hands.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Yuxuan Yang, Siyuan Zhou, He Weng, Dongjing Wang, Xin Zhang, Dongjin Yu, Shuiguang Deng
Summary: The study proposes a novel model, POIGDE, which addresses the challenges of data sparsity and elusive motives by solving graph differential equations to capture continuous variation of users' interests. The model learns interest transference dynamics using a time-serial graph and an interval-aware attention mechanism, and applies Siamese learning to directly learn from label representations for predicting future POI visits. The model outperforms state-of-the-art models on real-world datasets, showing potential in the POI recommendation domain.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
S. Karthika, P. Rathika
Summary: The widespread development of monitoring devices in the power system has generated a large amount of power consumption data. Storing and transmitting this data has become a significant challenge. This paper proposes an adaptive data compression algorithm based on the discrete wavelet transform (DWT) for power system applications. It utilizes multi-objective particle swarm optimization (MO-PSO) to select the optimal threshold. The algorithm has been tested and outperforms other existing algorithms.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Jiaqi Guo, Haiyan Wu, Xiaolei Chen, Weiguo Lin
Summary: In this study, an adaptive SV-Borderline SMOTE-SVM algorithm is proposed to address the challenge of imbalanced data classification. The algorithm maps the data into kernel space using SVM and identifies support vectors, then generates new samples based on the neighbors of these support vectors. Extensive experiments show that this method is more effective than other approaches in imbalanced data classification.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Qiumei Zheng, Linkang Xu, Fenghua Wang, Yongqi Xu, Chao Lin, Guoqiang Zhang
Summary: This paper proposes a new semantic segmentation network model called HilbertSCNet, which combines the Hilbert curve traversal and the dual pathway idea to design a new spatial computation module to address the problem of loss of information for small targets in high-resolution images. The experiments show that the proposed network performs well in the segmentation of small targets in high-resolution maps such as drone aerial photography.
APPLIED SOFT COMPUTING
(2024)
Article
Computer Science, Artificial Intelligence
Mojtaba Ashour, Amir Mahdiyar
Summary: Analytic Hierarchy Process (AHP) is a widely applied technique in multi-criteria decision-making problems, but the sheer number of AHP methods presents challenges for scholars and practitioners in selecting the most suitable method. This paper reviews articles published between 2010 and 2023 proposing hybrid, improved, or modified AHP methods, classifies them based on their contributions, and provides a comprehensive summary table and roadmap to guide the method selection process.
APPLIED SOFT COMPUTING
(2024)
Review
Computer Science, Artificial Intelligence
Gerardo Humberto Valencia-Rivera, Maria Torcoroma Benavides-Robles, Alonso Vela Morales, Ivan Amaya, Jorge M. Cruz-Duarte, Jose Carlos Ortiz-Bayliss, Juan Gabriel Avina-Cervantes
Summary: Electric power system applications are complex optimization problems. Most literature reviews focus on studying electrical paradigms using different optimization techniques, but there is a lack of review on Metaheuristics (MHs) in these applications. Our work provides an overview of the paradigms underlying such applications and analyzes the most commonly used MHs and their search operators. We also discover a strong synergy between the Renewable Energies paradigm and other paradigms, and a significant interest in Load-Forecasting optimization problems. Based on our findings, we provide helpful recommendations for current challenges and potential research paths to support further development in this field.
APPLIED SOFT COMPUTING
(2024)