Article
Computer Science, Information Systems
Xufeng Niu, Wenping Ma
Summary: To tackle the challenging task of high-dimensional data classification with limited labeled samples, we propose two semi-supervised learning models, SSRS and its adaptive version, ASSRS. These models address the unique characteristics of high-dimensional data by selecting subspaces of sample and feature dimensions and reducing dimensions. By incorporating sample-labeling auxiliary algorithm, adaptive sample subspace algorithm, and adaptive weight voting rule, ASSRS outperforms SSRS in terms of performance. Experiments demonstrate that SSRS and ASSRS perform better than other competitive algorithms and accurately label samples in datasets with limited labeled samples.
INFORMATION SCIENCES
(2023)
Article
Chemistry, Analytical
Whanhee Cho, Yongsuk Choi
Summary: This paper introduces a semi-supervised learning approach for text classification called LMGAN, based on generative adversarial networks (GAN). LMGAN utilizes BERT and GAN-BERT, multiple generators, and hidden layer outputs to enrich the distribution of fake data, addressing the limitation of early GAN-based methods in generating high-quality fake data. Experimental results demonstrate that LMGAN achieves better performance even with limited amounts of labeled data.
Article
Computer Science, Artificial Intelligence
Meng Han, Hongxin Wu, Zhiqiang Chen, Muhang Li, Xilong Zhang
Summary: This paper introduces multi-label classification algorithms based on supervised learning and semi-supervised learning. By using labeled and unlabeled data to train classifiers, semi-supervised learning can achieve better model performance. The paper also discusses multi-label classification algorithms in different application areas and presents evaluation metrics and datasets. Finally, future research directions are outlined.
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Zijia Zhang, Yaoming Cai, Wenyin Gong
Summary: This paper presents a novel semi-supervised learning framework, Graph Convolutional Extreme Learning Machines (GCELM), for handling graph data in non-Euclidean domains. The proposed methods achieve significantly better results than previous methods on 36 benchmark datasets, thanks to the use of random graph convolution and a voting ensemble strategy.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Review
Computer Science, Artificial Intelligence
Jose Marcio Duarte, Lilian Berton
Summary: A large amount of data is generated daily, leading to challenges in handling big data. One of the challenges is in text mining, particularly text classification. Semi-supervised learning (SSL), which utilizes labeled and unlabeled data, has become increasingly important in this field. This paper aims to fill the gap by providing an up-to-date review of SSL for text classification, analyzing the application domain, datasets, languages, text representations, machine learning algorithms, evaluation metrics, and future trends.
ARTIFICIAL INTELLIGENCE REVIEW
(2023)
Article
Automation & Control Systems
Chuanxia Jian, Kaijun Yang, Yinhui Ao
Summary: The study proposes a fault diagnosis method based on active and semi-supervised learning, which improves model performance by selecting uncertain unlabelled samples and using heterogeneous classifiers, suitable for fault diagnosis with a small training set.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Artificial Intelligence
Xiao Wang, Daisuke Kihara, Jiebo Luo, Guo-Jun Qi
Summary: The study introduces a new EnAET framework to enhance semi-supervised learning methods with self-supervised information. Experimental results demonstrate that the EnAET framework significantly improves the performance of semi-supervised algorithms, even in scenarios with a limited number of images, and can greatly enhance supervised learning as well.
IEEE TRANSACTIONS ON IMAGE PROCESSING
(2021)
Article
Computer Science, Artificial Intelligence
Nai Zhou, Nianmin Yao, Qibin Li, Jian Zhao, Yanan Zhang
Summary: In situations with limited labeled texts, neural networks are prone to over-fitting. To address this, we propose Multi-MCCR, a method based on consistent regularization and multiple model contrast learning. By using multiple models with the same structure and a C-BiKL loss strategy, we improve the classification ability by obtaining multiple output distributions and minimizing the inconsistency between model training and inference.
KNOWLEDGE-BASED SYSTEMS
(2023)
Article
Computer Science, Information Systems
Ammar Mohammed, Rania Kora
Summary: Deep learning-based models have outperformed classical machine learning models in various text classification tasks in the past decade. However, finding the most suitable deep learning classifier remains a challenge. This study proposes a new meta-learning ensemble method that combines baseline deep learning models using 2-tiers of meta-classifiers to improve classification performance. Experimental results demonstrate that the proposed method significantly enhances the accuracy of baseline deep models and outperforms state-of-the-art ensemble methods.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Fatima Rodrigues, Hugo Correia
Summary: Stress is a common feeling in people's day-to-day life, especially at work, causing health problems and absenteeism. Detecting stress has been a challenging task, but studies have established a correlation between stress and perceivable human features. This research aims to provide an alternative approach to detect stress in the workplace using non-invasive methods, analyzing video-based plethysmography and physiological signals.
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Wei Feng, Yinghui Quan, Gabriel Dauphin, Qiang Li, Lianru Gao, Wenjiang Huang, Junshi Xia, Wentao Zhu, Mengdao Xing
Summary: An adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for the classification of hyperspectral images with limited training data, which increases the number of training instances by mining high-quality unlabeled samples and utilizes SMOTE to overcome class imbalance. The effectiveness of the proposed method is demonstrated on three real hyperspectral remote sensing datasets through comparisons with ensemble methods and semi-supervised methods.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Information Systems
Jihong Ouyang, Dong Mao, Qingyi Meng
Summary: This paper proposes an Open-Set Semi-Supervised Learning (OS-SSL) method for scenarios where the labeled and unlabeled data come from mismatched distributions. The method includes a label propagation algorithm to improve the quality of pseudo-labels and a novel OOD detection score to filter out out of distribution (OOD) samples. The empirical results demonstrate the effectiveness and expandability of the proposed method.
MULTIMEDIA TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Esra Adiyeke, Mustafa Gokce Baydogan
Summary: This paper introduces alternative semi-supervised tree-based strategies that are robust to scale differences both in terms of feature and target variables. Proposing the use of a scale-invariant proximity measure by means of tree-based ensembles to preserve the original characteristics of the data, the paper updates the classical tree derivation procedure to a multi-criteria form to resolve scale inconsistencies.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Jovan Chavoshinejad, Seyed Amjad Seyedi, Fardin Akhlaghian Tab, Navid Salahian
Summary: Semi-supervised nonnegative matrix factorization combines the strengths of matrix factorization in learning part-based representation and can achieve high learning performance with limited labeled data and a large amount of unlabeled data. Recent research focuses on utilizing self-supervised learning to enhance semi-supervised learning. This paper proposes an effective Self-Supervised Semi-Supervised Nonnegative Matrix Factorization (S4NMF) model that directly extracts a consensus result from ensembled NMFs with similarity and dissimilarity regularizations. Experimental results on standard benchmark datasets demonstrate the effectiveness of the proposed model in semi-supervised clustering.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Information Systems
Yang Yang, Hongchen Wei, Zhen-Qiang Sun, Guang-Yu Li, Yuanchun Zhou, Hui Xiong, Jian Yang
Summary: S2OSC algorithm addresses the embedding confusion problem in open set classification by incorporating out-of-class instances filtering and model re-training in a transductive manner. It achieves state-of-the-art performance across a variety of tasks and can be extended to an incremental update framework effectively with streaming data.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2022)
Review
Computer Science, Artificial Intelligence
Wei Gao, Shuangshuang Ge
Summary: This study provides a comprehensive review of slope stability research based on artificial intelligence methods, focusing on slope stability computation and evaluation. The review covers studies using quasi-physical intelligence methods, simulated evolutionary methods, swarm intelligence methods, hybrid intelligence methods, artificial neural network methods, vector machine methods, and other intelligence methods. The merits, demerits, and state-of-the-art research advancement of these studies are analyzed, and possible research directions for slope stability investigation based on artificial intelligence methods are suggested.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Khuong Le Nguyen, Hoa Thi Trinh, Saeed Banihashemi, Thong M. Pham
Summary: This study investigated the influence of input parameters on the shear strength of RC squat walls and found that ensemble learning models, particularly XGBoost, can effectively predict the shear strength. The axial load had a greater influence than reinforcement ratio, and longitudinal reinforcement had a more significant impact compared to horizontal and vertical reinforcement. The performance of XGBoost model outperforms traditional design models and reducing input features still yields reliable predictions.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Bo Hu, Huiyan Zhang, Xiaoyi Wang, Li Wang, Jiping Xu, Qian Sun, Zhiyao Zhao, Lei Zhang
Summary: A deep hierarchical echo state network (DHESN) is proposed to address the limitations of shallow coupled structures. By using transfer entropy, candidate variables with strong causal relationships are selected and a hierarchical reservoir structure is established to improve prediction accuracy. Simulation results demonstrate that DHESN performs well in predicting algal bloom.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Limin Wang, Lingling Li, Qilong Li, Kuo Li
Summary: This paper discusses the urgency of learning complex multivariate probability distributions due to the increase in data variability and quantity. It introduces a highly scalable classifier called TAN, which utilizes maximum weighted spanning tree (MWST) for graphical modeling. The paper theoretically proves the feasibility of extending one-dependence MWST to model high-dependence relationships and proposes a heuristic search strategy to improve the fitness of the extended topology to data. Experimental results demonstrate that this algorithm achieves a good bias-variance tradeoff and competitive classification performance compared to other high-dependence or ensemble learning algorithms.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhejing Hu, Gong Chen, Yan Liu, Xiao Ma, Nianhong Guan, Xiaoying Wang
Summary: Anxiety is a prevalent issue and music therapy has been found effective in reducing anxiety. To meet the diverse needs of individuals, a novel model called the spatio-temporal therapeutic music transfer model (StTMTM) is proposed.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Nur Ezlin Zamri, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Siti Syatirah Sidik, Alyaa Alway, Nurul Atiqah Romli, Yueling Guo, Siti Zulaikha Mohd Jamaludin
Summary: In this study, a hybrid logic mining model was proposed by combining the logic mining approach with the Modified Niche Genetic Algorithm. This model improves the generalizability and storage capacity of the retrieved induced logic. Various modifications were made to address other issues. Experimental results demonstrate that the proposed model outperforms baseline methods in terms of accuracy, precision, specificity, and correlation coefficient.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
David Jacob Kedziora, Tien-Dung Nguyen, Katarzyna Musial, Bogdan Gabrys
Summary: The paper addresses the problem of efficiently optimizing machine learning solutions by reducing the configuration space of ML pipelines and leveraging historical performance. The experiments conducted show that opportunistic/systematic meta-knowledge can improve ML outcomes, and configuration-space culling is optimal when balanced. The utility and impact of meta-knowledge depend on various factors and are crucial for generating informative meta-knowledge bases.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
G. Sophia Jasmine, Rajasekaran Stanislaus, N. Manoj Kumar, Thangamuthu Logeswaran
Summary: In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for EV charging stations and capacitors in power grids to enhance voltage stability and reduce power losses. A hybrid approach combining the Fire Hawk Optimizer and Spiking Neural Network is proposed, which shows promising results in improving system performance. The optimization approach has the potential to enhance the stability and efficiency of electric grids.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Zhijiang Wu, Guofeng Ma
Summary: This study proposes a natural language processing-based framework for requirement retrieval and document association, which can help to mine and retrieve documents related to project managers' requirements. The framework analyzes the ontology relevance and emotional preference of requirements. The results show that the framework performs well in terms of iterations and threshold, and there is a significant matching between the retrieved documents and the requirements, which has significant managerial implications for construction safety management.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Yung-Kuan Chan, Chuen-Horng Lin, Yuan-Rong Ben, Ching-Lin Wang, Shu-Chun Yang, Meng-Hsiun Tsai, Shyr-Shen Yu
Summary: This study proposes a novel method for dog identification using nose-print recognition, which can be applied to controlling stray dogs, locating lost pets, and pet insurance verification. The method achieves high recognition accuracy through two-stage segmentation and feature extraction using a genetic algorithm.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Shaohua Song, Elena Tappia, Guang Song, Xianliang Shi, T. C. E. Cheng
Summary: This study aims to optimize supplier selection and demand allocation decisions for omni-channel retailers in order to achieve supply chain resilience. It proposes a two-phase approach that takes into account various factors such as supplier evaluation and demand allocation.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jinyan Hu, Yanping Jiang
Summary: This paper examines the allocation problem of shared parking spaces considering parking unpunctuality and no-shows. It proposes an effective approach using sample average approximation (SAA) combined with an accelerating Benders decomposition (ABD) algorithm to solve the problem. The numerical experiments demonstrate the significance of supply-demand balance for the operation and user satisfaction of the shared parking system.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Soroor Motie, Bijan Raahemi
Summary: Financial fraud is a persistent problem in the finance industry, but Graph Neural Networks (GNNs) have emerged as a powerful tool for detecting fraudulent activities. This systematic review provides a comprehensive overview of the current state-of-the-art technologies in using GNNs for financial fraud detection, identifies gaps and limitations in existing research, and suggests potential directions for future research.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Review
Computer Science, Artificial Intelligence
Enhao Ning, Changshuo Wang, Huang Zhang, Xin Ning, Prayag Tiwari
Summary: This review provides a detailed overview of occluded person re-identification methods and conducts a systematic analysis and comparison of existing deep learning-based approaches. It offers important theoretical and practical references for future research in the field.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Computer Science, Artificial Intelligence
Jiajun Ma, Songyu Hu, Jianzhong Fu, Gui Chen
Summary: The article presents a novel visual hierarchical attention detector for multi-scale defect location and classification, utilizing texture, semantic, and instance features of defects through a hierarchical attention mechanism, achieving multi-scale defect detection in bearing images with complex backgrounds.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)