Article
Computer Science, Artificial Intelligence
Seokho Kang
Summary: This paper proposes a binary classifier ensemble-based one-class classifier (BCE-OC) for one-class classification, which allows the use of any supervised classification algorithms and extensive comparison of various learning algorithms to obtain a more competent one-class classifier. Experimental validation using benchmark datasets demonstrates the effectiveness of BCE-OC.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Rogerio C. P. Fragoso, George D. C. Cavalcanti, Roberto H. W. Pinheiro, Luiz S. Oliveira
Summary: This work introduces a new method called MODES, which decomposes the original multi-class problem into multiple one-class problems to provide competent classifiers for each region of the feature space. Experimental results show that this method outperforms the literature, especially for databases with complex decision regions.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Energy & Fuels
Shiqian Wang, Ding Han, Yuanpeng Hua, Yuanyuan Wang, Lei Wang, Yang Liu
Summary: This article presents an improved selective ensemble learning approach to handle class imbalance and base classifier redundancy in load classification. Experimental results show that the approach is effective for load classification tasks.
FRONTIERS IN ENERGY RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Tuanfei Zhu, Cheng Luo, Zhihong Zhang, Jing Li, Siqi Ren, Yifu Zeng
Summary: This paper introduces a structure-preserving Oversampling method for high-dimensional imbalanced time series classification, OHIT, and integrates it into boosting framework to form a new ensemble algorithm OHITBoost. Extensive experiments on several publicly available time-series datasets demonstrate their effectiveness.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
JungHo Jeon, Hubo Cai
Summary: Improving workers' safety in the construction industry is of utmost importance. This study explores the use of wearable EEG devices and virtual reality to analyze workers' brain waves in relation to construction hazards and develops a classifier to identify these hazards. The initial results showed promising accuracy, and further strategies were implemented to improve the performance, resulting in a higher accuracy rate. The findings showcase the potential of coupling EEG, VR, and machine learning for hazard identification and contribute to the overall safety of construction workplaces.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Environmental Sciences
Hamid Jafarzadeh, Masoud Mahdianpari, Eric Gill, Fariba Mohammadimanesh, Saeid Homayouni
Summary: The study investigates the capability of different ensemble learning algorithms for satellite image classification, with XGBoost showing superior performance in multispectral, hyperspectral, and Polarimetric Synthetic Aperture Radar (PolSAR) data classification.
Article
Computer Science, Information Systems
Osama A. Mahdi, Eric Pardede, Nawfal Ali
Summary: Data stream mining is an important research topic with increasing attention in various applications. Challenges of concept drift and multiple classes in data streams have motivated the proposal of a hybrid block-based ensemble approach, which outperforms other algorithms in experimental evaluations.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Engineering, Electrical & Electronic
Benjamin Pannetier, Jean Dezert, Julien Moras, Raphael Levy
Summary: This paper describes a complete solution for a new dropped wireless sensor network dedicated to intelligence operation. The main contribution of this paper is the presentation of experimental results obtained with a Joint Tracking and Classification algorithm that utilizes contextual information for data fusion.
IEEE SENSORS JOURNAL
(2022)
Article
Engineering, Multidisciplinary
S. Sandhiyaa, C. Gomathy
Summary: This paper investigates a cross-layer approach in the MAC and routing layers of underwater acoustic sensor networks (UWASNs), which achieves better performance in underwater networks through load balancing in the MAC layer and efficient routing using a hybrid algorithm.
ALEXANDRIA ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Toshitaka Hayashi, Hamido Fujita
Summary: This paper addresses the issue of imbalanced data classification by proposing a reliable strategy using an ensemble of one-class classifiers, which avoids the trust issues and security instabilities caused by oversampling methods. Experimental results show that the one-class ensemble classifier outperforms sampling methods in 20 datasets.
APPLIED INTELLIGENCE
(2022)
Article
Computer Science, Information Systems
Farah Jemili
Summary: Intelligent intrusion detection system is a promising technique for securing computer networks due to the rapid evolution of attacks and network growth. Individual classification methods have proven to be inefficient in providing good detection rates and reducing false alarm rates. In this study, a hybrid approach based on the stacking scheme is proposed, which combines the strengths of neuro-fuzzy and genetic-fuzzy methods to maximize detection rates and reduce false alarm rates effectively.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Xianxian Li, Jing Liu, Songfeng Liu, Jinyan Wang
Summary: This study applies differential privacy techniques to ensemble learning, proposing an algorithm that achieves a balance between privacy protection and prediction accuracy in classification.
Article
Computer Science, Artificial Intelligence
Stefano Mauceri, James Sweeney, Miguel Nicolau, James McDermott
Summary: We propose a method to embed time series into a latent space where pairwise Euclidean distances equal pairwise dissimilarities in the original space. By using auto-encoder and encoder-only neural networks to learn elastic dissimilarity measures like dynamic time warping, we achieve classification performance close to that of raw data but with significantly lower dimensionality. This provides substantial savings in computational and storage requirements for nearest neighbor time series classification.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Information Systems
Michal Wozniak, Pawel Zyblewski, Pawel Ksieniewicz
Summary: Concept drift is a significant problem in data stream classification, causing performance degradation. This paper proposes a novel algorithm, AWAE, which utilizes ensemble learning and active learning to address concept drift effectively. Experimental results demonstrate its high quality compared to state-of-the-art methods.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Xiaoyan Zhu, Jiaxuan Li, Jingtao Ren, Jiayin Wang, Guangtao Wang
Summary: This study proposes a new method called MLDE for solving the multi-label classification problem. It selects the most competent ensemble of base classifiers to predict each unseen instance, effectively utilizing label correlation and achieving better performance.
INFORMATION SCIENCES
(2023)
Article
Agriculture, Multidisciplinary
Md Sumon Shahriar, Daniel Smith, Ashfaqur Rahman, Mark Freeman, James Hills, Richard Rawnsley, Dave Henry, Greg Bishop-Hurley
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2016)
Article
Agriculture, Multidisciplinary
Daniel Smith, Ashfaqur Rahman, Greg J. Bishop-Hurley, James Hills, Sumon Shahriar, David Henry, Richard Rawnsley
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2016)
Article
Engineering, Electrical & Electronic
Ke Hu, Vijay Sivaraman, Blanca Gallego Luxan, Ashfaqur Rahman
IEEE SENSORS JOURNAL
(2016)
Article
Engineering, Electrical & Electronic
Ke Hu, Ashfaqur Rahman, Hari Bhrugubanda, Vijay Sivaraman
IEEE SENSORS JOURNAL
(2017)
Article
Agriculture, Multidisciplinary
P. L. Greenwood, D. R. Paull, J. McNally, T. Kalinowski, D. Ebert, B. Little, D. V. Smith, A. Rahman, P. Valencia, A. B. Ingham, G. J. Bishop-Hurley
CROP & PASTURE SCIENCE
(2017)
Article
Computer Science, Artificial Intelligence
Sumaira Tasnim, Ashfaqur Rahman, Amanullah Maung Than Oo, Md Enamul Haque
KNOWLEDGE-BASED SYSTEMS
(2018)
Article
Agriculture, Multidisciplinary
Ashfaqur Rahman, Stuart Arnold, Joel Janek Dabrowski
COMPUTERS AND ELECTRONICS IN AGRICULTURE
(2019)
Article
Automation & Control Systems
Akhlaqur Rahman, Jiong Jin, Antonio L. Cricenti, Ashfaqur Rahman, Ambarish Kulkarni
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2019)
Article
Parasitology
Amy Bell, Jody McNally, Daniel V. Smith, Ashfaqur Rahman, Peter Hunt, Andrew C. Kotze, Sonja Dominik, Aaron Ingham
VETERINARY PARASITOLOGY
(2019)
Article
Computer Science, Theory & Methods
Mahbuba Afrin, Jiong Jin, Ashfaqur Rahman, Yu-Chu Tian, Ambarish Kulkarni
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2019)
Article
Computer Science, Artificial Intelligence
Mahbub E. Khoda, Joarder Kamruzzaman, Iqbal Gondal, Tasadduq Imam, Ashfaqur Rahman
Summary: This paper introduces a novel malware oversampling technique to address the performance degradation issue in machine learning methods for imbalanced data. By combining fuzzy set theory with a novel loss function, two malware detection approaches are proposed, achieving over 9% improvement in terms of F1_score. This can lead to enhanced privacy and security in edge computing services.
APPLIED SOFT COMPUTING
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Ashfaqur Rahman, Philip Smethurst, Michael Attard, Rob Dunne
TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING, 2017
(2017)
Proceedings Paper
Computer Science, Artificial Intelligence
Robert Dunne, Dave Henry, Richard Rawnsley, Ashfaqur Rahman
TRENDS AND APPLICATIONS IN KNOWLEDGE DISCOVERY AND DATA MINING, 2017
(2017)
Article
Computer Science, Artificial Intelligence
Sumaira Tasnim, Ashfaqur Rahman, Amanullah Maung Than Oo, Md Enamul Haque
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS
(2017)
Article
Telecommunications
Akhlaqur Rahman, Jiong Jin, Antonio Cricenti, Ashfaqur Rahman, Marimuthu Palaniswami, Tie Luo
JOURNAL OF SENSOR AND ACTUATOR NETWORKS
(2016)