Article
Computer Science, Information Systems
Huanze Zeng, Argon Chen
Summary: A simple multi-layer classifier (MLC) model with binary split is proposed in the study, which has been thoroughly tested with 40 datasets, showing that binary MLC models are easier to interpret and achieve significantly better performance compared to other models.
INFORMATION SCIENCES
(2021)
Article
Engineering, Electrical & Electronic
Poras Khetarpal, Madan Mohan Tripathi
Summary: Power quality disturbance (PQD) is a significant issue in power system analysis, and accurate detection is crucial for the proper functioning of various industries and smart systems. Machine learning-based techniques provide good accuracy for PQD classification, and adding a fuzzification step during pre-processing further improves classification accuracy. This approach outperforms other popular PQD classification methods, both fuzzy and non-fuzzy based.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Computer Science, Artificial Intelligence
Patrick P. K. Chan, Juan Zheng, Han Liu, E. C. C. Tsang, Daniel S. Yeung
Summary: This study examines the robustness of classical decision tree (DT) and fuzzy decision tree (FDT) in an adversarial environment. Experimental results show that the fuzzifying process increases the robustness of DT, but FDT with more membership functions is more vulnerable to attacks.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Pietro Ducange, Francesco Marcelloni, Riccardo Pecori
Summary: Data stream mining is gaining popularity due to the need for continuous analysis of streaming data, which requires appropriate techniques as traditional machine learning algorithms struggle with fast data streams. This paper introduces a fuzzy Hoeffding Decision Tree (FHDT) that enhances the traditional HDT to be more robust to noisy and vague data. FHDT outperforms HDT, especially in the presence of concept drift, and is highly interpretable thanks to the linguistic rules that can be extracted from it.
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Farnaz Mahan, Maryam Mohammadzad, Seyyed Meysam Rozekhani, Witold Pedrycz
Summary: Several classification algorithms using fuzzy decision trees have been proposed in recent years, with a key issue being the selection of the division feature. While traditional algorithms commonly use the information gain criterion, it has been pointed out that this can lead to an unfair selection of features with a large number of values. The paper presents a new algorithm that extends MFlexDT and utilizes chi-square based fuzzy partitioning to improve accuracy in stream data classification.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Xiaoyu Han, Xiubin Zhu, Witold Pedrycz, Zhiwu Li
Summary: This study designs a three-way classification mechanism by combining fuzzy decision trees and expressing uncertainty. A fuzzy decision tree is constructed through generalization and the three-way decision model is widely used. An efficient way to flag uncertain data is proposed, which is not possible with commonly used fuzzy decision trees. The developed mechanism consists of two stages: building a fuzzy decision tree and determining the uncertainty level to reject instances. The rejection quality is quantified in terms of accuracy and coefficient, and the mechanism performs better than other three-way decision models.
APPLIED SOFT COMPUTING
(2023)
Article
Environmental Sciences
Jionghua Wang, Haowen Luo, Wenyu Li, Bo Huang
Summary: This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data, and verified on a dataset from Shenzhen, China. It was found that basic building attributes and POIs contributed most to the classification process, while crowdsensing data becomes increasingly important in more complicated classification tasks.
Article
Computer Science, Artificial Intelligence
Huanze Zeng, Argon Chen
Summary: This research proposes a multivariate multi-layer classifier that applies a variance-ratio criterion to enable ternary splits of each tree node and integrates the oblique discriminant hyperplane in the tree node, resulting in enhanced classification performance.
PATTERN RECOGNITION
(2022)
Article
Computer Science, Artificial Intelligence
Xiaowei Gu
Summary: This paper proposes a novel dual-model self-organizing fuzzy inference system for semi-supervised learning from data streams in infinite delay environments. The proposed model learns and expands its knowledge base from unlabelled data with minimal human expert involvement. It continuously identifies new prototypes and self-improves its knowledge base while minimizing the impact of pseudo-labelled errors on decision-making. Numerical examples demonstrate the efficacy of the proposed method in offering higher classification accuracy and computational efficiency compared to state-of-the-art competitors.
APPLIED SOFT COMPUTING
(2023)
Article
Environmental Sciences
Yinghui Zhao, Ye Ma, Lindi J. Quackenbush, Zhen Zhen
Summary: In this study, the performance of machine learning and ensemble learning algorithms in tree species classification and individual tree AGB estimation based on ALS data and WorldView-3 imagery was investigated. The results showed that the combination of ALS and WorldView-3 performed better in tree species classification, and ensemble learning algorithms outperformed machine learning algorithms. This study provides an effective method for tree species classification and individual tree AGB estimation of NSFs.
Article
Computer Science, Artificial Intelligence
Md Anisur Rahman, Mirko Duradoni, Andrea Guazzini
Summary: Research on Phubbing has traditionally focused on linear statistics, but more recent studies have utilized data mining and machine learning to identify patterns related to Phubbing behavior. These studies have shown that Phubbing is not solely determined by addiction measures, but also by non-linear relationships with ICT measures and social anxiety. Machine learning approaches have been found to be more effective at predicting Phubbing compared to traditional linear statistics.
NEURAL COMPUTING & APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Te Zhang, Zhaohong Deng, Hisao Ishibuchi, Lie Meng Pang
Summary: TSK fuzzy systems have been widely applied in supervised learning, but label noise in real-world data can have a negative impact on the learning process. This article introduces a robust algorithm, RTSK-FS-SS, based on semi-supervised learning and intuitionistic fuzzy set method to detect and handle label noise in data.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2021)
Article
Computer Science, Information Systems
Yingzhong Shi, Andong Li, Zhaohong Deng, Qisheng Yan, Qiongdan Lou, Haoran Chen, Kup-Sze Choi, Shitong Wang
Summary: Data stream classification methods exploiting cohesion in a single data stream have shown promising performance. However, scenarios involving multiple correlated data streams are common in practice. Therefore, leveraging the correlations among multi-task data streams can improve the effectiveness of data stream modeling.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jinlin Guo, Haoran Wang, Xinwei Li, Li Zhang
Summary: A dynamic stream data classification algorithm is proposed to address the incomplete labeling problem and concept drift problem in stream data. By introducing randomization, iterative strategy, and window mechanism, the algorithm demonstrates superior performance in classification accuracy.
MOBILE INFORMATION SYSTEMS
(2021)
Article
Ecology
Sonam Sah, Dipanwita Haldar, Subhash Chandra, Ajeet Singh Nain
Summary: This study introduces a novel approach using Synthetic Aperture Radar (SAR) data to discriminate between different rice cultural types and subtypes and identify their optimum stages for representing crop growth profiles. The findings show that C-band co and cross-polarized SAR data can accurately detect differences between different rice types and subtypes, enabling effective crop growth monitoring.
ECOLOGICAL INFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Hamed Haddad Pajouh, GholamHossein Dastghaibyfard, Sattar Hashemi
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
(2017)
Article
Computer Science, Information Systems
Niloofar Mozafari, Ali Hamzeh, Sattar Hashemi
JOURNAL OF INFORMATION SCIENCE
(2017)
Article
Computer Science, Artificial Intelligence
Jafar Tahmoresnezhad, Sattar Hashemi
KNOWLEDGE AND INFORMATION SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Fatemeh Jahedpari, Talal Rahwan, Sattar Hashemi, Tomasz P. Michalak, Marina De Vos, Julian Padget, Wei Lee Woon
IEEE INTELLIGENT SYSTEMS
(2017)
Article
Computer Science, Artificial Intelligence
Jafar Tahmoresnezhad, Sattar Hashemi
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
(2017)
Article
Computer Science, Artificial Intelligence
Zeinab Khorshidpour, Jafar Tahmoresnezhad, Sattar Hashemi, Ali Hamzeh
INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS
(2018)
Article
Computer Science, Information Systems
Sajad Homayoun, Ali Dehghantanha, Marzieh Ahmadzadeh, Sattar Hashemi, Raouf Khayami
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
(2020)
Article
Green & Sustainable Science & Technology
Mehrnoosh Torabi, Sattar Hashemi, Mahmoud Reza Saybani, Shahaboddin Shamshirband, Amir Mosavi
ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY
(2019)
Article
Computer Science, Theory & Methods
Sajad Homayoun, Ali Dehghantanha, Marzieh Ahmadzadeh, Sattar Hashemi, Raouf Khayami, Kim-Kwang Raymond Choo, David Ellis Newton
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE
(2019)
Article
Computer Science, Artificial Intelligence
Esmaeel Radkani, Sattar Hashemi, Alireza Keshavarz-Haddad, Maryam Amir Haeri
APPLIED INTELLIGENCE
(2018)
Proceedings Paper
Computer Science, Artificial Intelligence
Seyed Mehdi Hazrati Fard, Sattar Hashemi
2017 19TH CSI INTERNATIONAL SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND SIGNAL PROCESSING (AISP)
(2017)
Proceedings Paper
Computer Science, Theory & Methods
Amir Namavar Jahromi, Sattar Hashemi
2017 9TH INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE TECHNOLOGY (IKT 2017)
(2017)
Article
Computer Science, Artificial Intelligence
Zeinab Khorshidpour, Sattar Hashemi, Ali Hamzeh
APPLIED INTELLIGENCE
(2017)
Article
Computer Science, Information Systems
Hashem Hashemi, Amin Azmoodeh, Ali Hamzeh, Sattar Hashemi
JOURNAL OF COMPUTER VIROLOGY AND HACKING TECHNIQUES
(2017)
Article
Engineering, Multidisciplinary
J. Tahmoresnezhad, S. Hashemi